{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4a13f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cc7cc22",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "941f35a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>客户编号</th>\n",
       "      <th>已发货款</th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比列</th>\n",
       "      <th>品牌</th>\n",
       "      <th>骑车销售商</th>\n",
       "      <th>车厂</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>货款日期</th>\n",
       "      <th>地区</th>\n",
       "      <th>...</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已还贷款比列</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>次账户还款期数</th>\n",
       "      <th>贷款与已批准贷款比列</th>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <th>工作类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>601758</td>\n",
       "      <td>65532</td>\n",
       "      <td>78990</td>\n",
       "      <td>84.38</td>\n",
       "      <td>136</td>\n",
       "      <td>20490</td>\n",
       "      <td>45</td>\n",
       "      <td>1981</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>519488</td>\n",
       "      <td>56759</td>\n",
       "      <td>65325</td>\n",
       "      <td>89.55</td>\n",
       "      <td>61</td>\n",
       "      <td>22778</td>\n",
       "      <td>86</td>\n",
       "      <td>1967</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2054139</td>\n",
       "      <td>2036500</td>\n",
       "      <td>2036500</td>\n",
       "      <td>34455</td>\n",
       "      <td>0.99</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>447579</td>\n",
       "      <td>58413</td>\n",
       "      <td>67960</td>\n",
       "      <td>89.02</td>\n",
       "      <td>5</td>\n",
       "      <td>15663</td>\n",
       "      <td>86</td>\n",
       "      <td>1977</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>648134</td>\n",
       "      <td>72317</td>\n",
       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>76</td>\n",
       "      <td>17242</td>\n",
       "      <td>48</td>\n",
       "      <td>1995</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13813</td>\n",
       "      <td>13813</td>\n",
       "      <td>0</td>\n",
       "      <td>13814.00</td>\n",
       "      <td>13813</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458210</td>\n",
       "      <td>50078</td>\n",
       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>146</td>\n",
       "      <td>14181</td>\n",
       "      <td>45</td>\n",
       "      <td>1974</td>\n",
       "      <td>2018</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>467161</td>\n",
       "      <td>550000</td>\n",
       "      <td>550000</td>\n",
       "      <td>12863</td>\n",
       "      <td>1.18</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     客户编号   已发货款   资产成本  贷款与资产比列   品牌  骑车销售商  车厂  出生日期  货款日期  地区  ...  \\\n",
       "0  601758  65532  78990    84.38  136  20490  45  1981  2018   8  ...   \n",
       "1  519488  56759  65325    89.55   61  22778  86  1967  2018   6  ...   \n",
       "2  447579  58413  67960    89.02    5  15663  86  1977  2018   9  ...   \n",
       "3  648134  72317  99750    73.68   76  17242  48  1995  2018   8  ...   \n",
       "4  458210  50078  65450    79.45  146  14181  45  1974  2018  17  ...   \n",
       "\n",
       "   尚未还清有效贷款总额  已批准贷款总额  已发放贷款总额  每月还款总额  贷款与已还贷款比列  主账户还款期数  次账户还款期数  \\\n",
       "0           0        0        0       0       1.00        0        0   \n",
       "1     2054139  2036500  2036500   34455       0.99       59        0   \n",
       "2           0        0        0       0       1.00        0        0   \n",
       "3           0    13813    13813       0   13814.00    13813        0   \n",
       "4      467161   550000   550000   12863       1.18       42        0   \n",
       "\n",
       "   贷款与已批准贷款比列  总贷款次数与总有效贷款次数比  工作类型  \n",
       "0         1.0            1.00     0  \n",
       "1         1.0            1.33     1  \n",
       "2         1.0            1.00     1  \n",
       "3         1.0            2.00     0  \n",
       "4         1.0            1.06     1  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = pd.read_csv(r'C:\\Users\\LouisLou\\Desktop\\作业资料下载\\车贷违约预测.csv')\n",
    "file.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ad701489",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199717 entries, 0 to 199716\n",
      "Data columns (total 49 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   客户编号            199717 non-null  int64  \n",
      " 1   已发货款            199717 non-null  int64  \n",
      " 2   资产成本            199717 non-null  int64  \n",
      " 3   贷款与资产比列         199717 non-null  float64\n",
      " 4   品牌              199717 non-null  int64  \n",
      " 5   骑车销售商           199717 non-null  int64  \n",
      " 6   车厂              199717 non-null  int64  \n",
      " 7   出生日期            199717 non-null  int64  \n",
      " 8   货款日期            199717 non-null  int64  \n",
      " 9   地区              199717 non-null  int64  \n",
      " 10  对接员工编号          199717 non-null  int64  \n",
      " 11  是否填写手机号         199717 non-null  int64  \n",
      " 12  受否填写身份证         199717 non-null  int64  \n",
      " 13  是否出具驾驶证         199717 non-null  int64  \n",
      " 14  是否填写护照          199717 non-null  int64  \n",
      " 15  信用评分            199717 non-null  int64  \n",
      " 16  主账户贷款次数         199717 non-null  int64  \n",
      " 17  主账户有效贷款次数       199717 non-null  int64  \n",
      " 18  主账户中尚未还清有效贷款    199717 non-null  int64  \n",
      " 19  主账户中已批准的贷款      199717 non-null  int64  \n",
      " 20  主账户中已发放贷款       199717 non-null  int64  \n",
      " 21  次账户贷款次数         199717 non-null  int64  \n",
      " 22  次账户有效贷款次数       199717 non-null  int64  \n",
      " 23  次账户中尚未还清有效贷款    199717 non-null  int64  \n",
      " 24  次账户中已批准贷款       199717 non-null  int64  \n",
      " 25  次账户中已发放贷款       199717 non-null  int64  \n",
      " 26  主账户每月还款         199717 non-null  int64  \n",
      " 27  次账户没用还款         199717 non-null  int64  \n",
      " 28  近六个月新贷款次数       199717 non-null  int64  \n",
      " 29  近六个月违约次数        199717 non-null  int64  \n",
      " 30  平均贷款期限          199717 non-null  int64  \n",
      " 31  第一次贷款距今时间       199717 non-null  int64  \n",
      " 32  贷款查询次数          199717 non-null  int64  \n",
      " 33  是否违约            199717 non-null  int64  \n",
      " 34  贷款与资产比          199717 non-null  float64\n",
      " 35  贷款总次数           199717 non-null  int64  \n",
      " 36  主账户无效贷款次数       199717 non-null  int64  \n",
      " 37  次账户无效贷款次数       199717 non-null  int64  \n",
      " 38  无效贷款总次数         199717 non-null  int64  \n",
      " 39  尚未还清有效贷款总额      199717 non-null  int64  \n",
      " 40  已批准贷款总额         199717 non-null  int64  \n",
      " 41  已发放贷款总额         199717 non-null  int64  \n",
      " 42  每月还款总额          199717 non-null  int64  \n",
      " 43  贷款与已还贷款比列       199717 non-null  float64\n",
      " 44  主账户还款期数         199717 non-null  int64  \n",
      " 45  次账户还款期数         199717 non-null  int64  \n",
      " 46  贷款与已批准贷款比列      199717 non-null  float64\n",
      " 47  总贷款次数与总有效贷款次数比  199717 non-null  float64\n",
      " 48  工作类型            199717 non-null  int64  \n",
      "dtypes: float64(5), int64(44)\n",
      "memory usage: 74.7 MB\n"
     ]
    }
   ],
   "source": [
    "file.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d79e803",
   "metadata": {},
   "outputs": [],
   "source": [
    "file.rename(columns={'受否填写身份证':'是否填写身份证'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0cb67126",
   "metadata": {},
   "outputs": [],
   "source": [
    "file.rename(columns={'骑车销售商':'汽车销售商'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "466c9087",
   "metadata": {},
   "outputs": [],
   "source": [
    "file.rename(columns={'次账户没用还款':'次账户每月还款'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "786431ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "file.rename(columns={'贷款与资产比列':'贷款与资产比例'},inplace=True)\n",
    "file.rename(columns={'贷款与已还贷款比列':'贷款与已还贷款比例'},inplace=True)\n",
    "file.rename(columns={'贷款与已批准贷款比列':'贷款与已批准贷款比例'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "300cbd26",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199717 entries, 0 to 199716\n",
      "Data columns (total 49 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   客户编号            199717 non-null  int64  \n",
      " 1   已发货款            199717 non-null  int64  \n",
      " 2   资产成本            199717 non-null  int64  \n",
      " 3   贷款与资产比例         199717 non-null  float64\n",
      " 4   品牌              199717 non-null  int64  \n",
      " 5   汽车销售商           199717 non-null  int64  \n",
      " 6   车厂              199717 non-null  int64  \n",
      " 7   出生日期            199717 non-null  int64  \n",
      " 8   货款日期            199717 non-null  int64  \n",
      " 9   地区              199717 non-null  int64  \n",
      " 10  对接员工编号          199717 non-null  int64  \n",
      " 11  是否填写手机号         199717 non-null  int64  \n",
      " 12  是否填写身份证         199717 non-null  int64  \n",
      " 13  是否出具驾驶证         199717 non-null  int64  \n",
      " 14  是否填写护照          199717 non-null  int64  \n",
      " 15  信用评分            199717 non-null  int64  \n",
      " 16  主账户贷款次数         199717 non-null  int64  \n",
      " 17  主账户有效贷款次数       199717 non-null  int64  \n",
      " 18  主账户中尚未还清有效贷款    199717 non-null  int64  \n",
      " 19  主账户中已批准的贷款      199717 non-null  int64  \n",
      " 20  主账户中已发放贷款       199717 non-null  int64  \n",
      " 21  次账户贷款次数         199717 non-null  int64  \n",
      " 22  次账户有效贷款次数       199717 non-null  int64  \n",
      " 23  次账户中尚未还清有效贷款    199717 non-null  int64  \n",
      " 24  次账户中已批准贷款       199717 non-null  int64  \n",
      " 25  次账户中已发放贷款       199717 non-null  int64  \n",
      " 26  主账户每月还款         199717 non-null  int64  \n",
      " 27  次账户每月还款         199717 non-null  int64  \n",
      " 28  近六个月新贷款次数       199717 non-null  int64  \n",
      " 29  近六个月违约次数        199717 non-null  int64  \n",
      " 30  平均贷款期限          199717 non-null  int64  \n",
      " 31  第一次贷款距今时间       199717 non-null  int64  \n",
      " 32  贷款查询次数          199717 non-null  int64  \n",
      " 33  是否违约            199717 non-null  int64  \n",
      " 34  贷款与资产比          199717 non-null  float64\n",
      " 35  贷款总次数           199717 non-null  int64  \n",
      " 36  主账户无效贷款次数       199717 non-null  int64  \n",
      " 37  次账户无效贷款次数       199717 non-null  int64  \n",
      " 38  无效贷款总次数         199717 non-null  int64  \n",
      " 39  尚未还清有效贷款总额      199717 non-null  int64  \n",
      " 40  已批准贷款总额         199717 non-null  int64  \n",
      " 41  已发放贷款总额         199717 non-null  int64  \n",
      " 42  每月还款总额          199717 non-null  int64  \n",
      " 43  贷款与已还贷款比例       199717 non-null  float64\n",
      " 44  主账户还款期数         199717 non-null  int64  \n",
      " 45  次账户还款期数         199717 non-null  int64  \n",
      " 46  贷款与已批准贷款比例      199717 non-null  float64\n",
      " 47  总贷款次数与总有效贷款次数比  199717 non-null  float64\n",
      " 48  工作类型            199717 non-null  int64  \n",
      "dtypes: float64(5), int64(44)\n",
      "memory usage: 74.7 MB\n"
     ]
    }
   ],
   "source": [
    "file.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b068d3f8",
   "metadata": {},
   "source": [
    "### 删除数据及特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9f2b5ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(199717, 49)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7aa1a699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>客户编号</th>\n",
       "      <th>已发货款</th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比例</th>\n",
       "      <th>品牌</th>\n",
       "      <th>汽车销售商</th>\n",
       "      <th>车厂</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>货款日期</th>\n",
       "      <th>地区</th>\n",
       "      <th>...</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已还贷款比例</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>次账户还款期数</th>\n",
       "      <th>贷款与已批准贷款比例</th>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <th>工作类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>519488</td>\n",
       "      <td>56759</td>\n",
       "      <td>65325</td>\n",
       "      <td>89.55</td>\n",
       "      <td>61</td>\n",
       "      <td>22778</td>\n",
       "      <td>86</td>\n",
       "      <td>1967</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2054139</td>\n",
       "      <td>2036500</td>\n",
       "      <td>2036500</td>\n",
       "      <td>34455</td>\n",
       "      <td>0.99</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>648134</td>\n",
       "      <td>72317</td>\n",
       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>76</td>\n",
       "      <td>17242</td>\n",
       "      <td>48</td>\n",
       "      <td>1995</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13813</td>\n",
       "      <td>13813</td>\n",
       "      <td>0</td>\n",
       "      <td>13814.00</td>\n",
       "      <td>13813</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458210</td>\n",
       "      <td>50078</td>\n",
       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>146</td>\n",
       "      <td>14181</td>\n",
       "      <td>45</td>\n",
       "      <td>1974</td>\n",
       "      <td>2018</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>467161</td>\n",
       "      <td>550000</td>\n",
       "      <td>550000</td>\n",
       "      <td>12863</td>\n",
       "      <td>1.18</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>616513</td>\n",
       "      <td>63882</td>\n",
       "      <td>79605</td>\n",
       "      <td>82.91</td>\n",
       "      <td>152</td>\n",
       "      <td>14470</td>\n",
       "      <td>51</td>\n",
       "      <td>1993</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>16225</td>\n",
       "      <td>17700</td>\n",
       "      <td>17700</td>\n",
       "      <td>1475</td>\n",
       "      <td>1.09</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>453368</td>\n",
       "      <td>54013</td>\n",
       "      <td>62371</td>\n",
       "      <td>89.79</td>\n",
       "      <td>34</td>\n",
       "      <td>16556</td>\n",
       "      <td>86</td>\n",
       "      <td>1971</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>12991</td>\n",
       "      <td>100000</td>\n",
       "      <td>100000</td>\n",
       "      <td>3207</td>\n",
       "      <td>7.70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199709</th>\n",
       "      <td>470368</td>\n",
       "      <td>52199</td>\n",
       "      <td>63387</td>\n",
       "      <td>88.35</td>\n",
       "      <td>101</td>\n",
       "      <td>24379</td>\n",
       "      <td>86</td>\n",
       "      <td>1985</td>\n",
       "      <td>2018</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>72033</td>\n",
       "      <td>75000</td>\n",
       "      <td>80288</td>\n",
       "      <td>5354</td>\n",
       "      <td>1.11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199711</th>\n",
       "      <td>432468</td>\n",
       "      <td>63447</td>\n",
       "      <td>73701</td>\n",
       "      <td>88.19</td>\n",
       "      <td>13</td>\n",
       "      <td>14614</td>\n",
       "      <td>86</td>\n",
       "      <td>1976</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>128709</td>\n",
       "      <td>214103</td>\n",
       "      <td>214103</td>\n",
       "      <td>354750</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199712</th>\n",
       "      <td>436304</td>\n",
       "      <td>36439</td>\n",
       "      <td>60424</td>\n",
       "      <td>62.89</td>\n",
       "      <td>10</td>\n",
       "      <td>23507</td>\n",
       "      <td>45</td>\n",
       "      <td>1986</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>592668</td>\n",
       "      <td>525000</td>\n",
       "      <td>525000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>525000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199714</th>\n",
       "      <td>466468</td>\n",
       "      <td>54413</td>\n",
       "      <td>62710</td>\n",
       "      <td>89.30</td>\n",
       "      <td>67</td>\n",
       "      <td>16565</td>\n",
       "      <td>45</td>\n",
       "      <td>1973</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1185601</td>\n",
       "      <td>1220000</td>\n",
       "      <td>1220000</td>\n",
       "      <td>2500</td>\n",
       "      <td>1.03</td>\n",
       "      <td>487</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199716</th>\n",
       "      <td>638308</td>\n",
       "      <td>63147</td>\n",
       "      <td>72000</td>\n",
       "      <td>89.58</td>\n",
       "      <td>2</td>\n",
       "      <td>23169</td>\n",
       "      <td>45</td>\n",
       "      <td>1970</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>97963</td>\n",
       "      <td>106508</td>\n",
       "      <td>106508</td>\n",
       "      <td>0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>106508</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101014 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          客户编号   已发货款   资产成本  贷款与资产比例   品牌  汽车销售商  车厂  出生日期  货款日期  地区  ...  \\\n",
       "1       519488  56759  65325    89.55   61  22778  86  1967  2018   6  ...   \n",
       "3       648134  72317  99750    73.68   76  17242  48  1995  2018   8  ...   \n",
       "4       458210  50078  65450    79.45  146  14181  45  1974  2018  17  ...   \n",
       "5       616513  63882  79605    82.91  152  14470  51  1993  2018   3  ...   \n",
       "6       453368  54013  62371    89.79   34  16556  86  1971  2018   6  ...   \n",
       "...        ...    ...    ...      ...  ...    ...  ..   ...   ...  ..  ...   \n",
       "199709  470368  52199  63387    88.35  101  24379  86  1985  2018  15  ...   \n",
       "199711  432468  63447  73701    88.19   13  14614  86  1976  2018   8  ...   \n",
       "199712  436304  36439  60424    62.89   10  23507  45  1986  2018   3  ...   \n",
       "199714  466468  54413  62710    89.30   67  16565  45  1973  2018   6  ...   \n",
       "199716  638308  63147  72000    89.58    2  23169  45  1970  2018   4  ...   \n",
       "\n",
       "        尚未还清有效贷款总额  已批准贷款总额  已发放贷款总额  每月还款总额  贷款与已还贷款比例  主账户还款期数  次账户还款期数  \\\n",
       "1          2054139  2036500  2036500   34455       0.99       59        0   \n",
       "3                0    13813    13813       0   13814.00    13813        0   \n",
       "4           467161   550000   550000   12863       1.18       42        0   \n",
       "5            16225    17700    17700    1475       1.09       11        0   \n",
       "6            12991   100000   100000    3207       7.70       31        0   \n",
       "...            ...      ...      ...     ...        ...      ...      ...   \n",
       "199709       72033    75000    80288    5354       1.11       14        0   \n",
       "199711      128709   214103   214103  354750       1.66        0        0   \n",
       "199712      592668   525000   525000       0       0.89   525000        0   \n",
       "199714     1185601  1220000  1220000    2500       1.03      487        0   \n",
       "199716       97963   106508   106508       0       1.09   106508        0   \n",
       "\n",
       "        贷款与已批准贷款比例  总贷款次数与总有效贷款次数比  工作类型  \n",
       "1             1.00            1.33     1  \n",
       "3             1.00            2.00     0  \n",
       "4             1.00            1.06     1  \n",
       "5             1.00            1.50     0  \n",
       "6             1.00            1.33     1  \n",
       "...            ...             ...   ...  \n",
       "199709        1.07            1.75     0  \n",
       "199711        1.00            1.44     1  \n",
       "199712        1.00            3.00     0  \n",
       "199714        1.00            3.00     1  \n",
       "199716        1.00            2.00     2  \n",
       "\n",
       "[101014 rows x 49 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除数据中未贷款过的客户\n",
    "user_act = file[file['贷款总次数'] != 0]\n",
    "user_act"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0a104eb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看是否存在重复用户\n",
    "user_act.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1228c6a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>客户编号</th>\n",
       "      <th>已发货款</th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比例</th>\n",
       "      <th>品牌</th>\n",
       "      <th>汽车销售商</th>\n",
       "      <th>车厂</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>货款日期</th>\n",
       "      <th>地区</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.0</td>\n",
       "      <td>101014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>534834.195240</td>\n",
       "      <td>54549.362128</td>\n",
       "      <td>7.493134e+04</td>\n",
       "      <td>75.812913</td>\n",
       "      <td>71.658275</td>\n",
       "      <td>19331.470598</td>\n",
       "      <td>70.801344</td>\n",
       "      <td>1981.919160</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>7.100907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>68293.785377</td>\n",
       "      <td>13443.878212</td>\n",
       "      <td>1.940150e+04</td>\n",
       "      <td>11.171984</td>\n",
       "      <td>67.305972</td>\n",
       "      <td>3517.021764</td>\n",
       "      <td>22.212900</td>\n",
       "      <td>9.540514</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.333711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>417428.000000</td>\n",
       "      <td>13652.000000</td>\n",
       "      <td>3.700000e+04</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10524.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1949.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>476138.250000</td>\n",
       "      <td>47349.000000</td>\n",
       "      <td>6.536800e+04</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>16103.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>533845.500000</td>\n",
       "      <td>53873.000000</td>\n",
       "      <td>7.022500e+04</td>\n",
       "      <td>78.120000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>18520.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1983.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>593554.750000</td>\n",
       "      <td>60389.500000</td>\n",
       "      <td>7.774600e+04</td>\n",
       "      <td>84.470000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>22882.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>671033.000000</td>\n",
       "      <td>990572.000000</td>\n",
       "      <td>1.628992e+06</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>24803.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                客户编号           已发货款          资产成本        贷款与资产比例  \\\n",
       "count  101014.000000  101014.000000  1.010140e+05  101014.000000   \n",
       "mean   534834.195240   54549.362128  7.493134e+04      75.812913   \n",
       "std     68293.785377   13443.878212  1.940150e+04      11.171984   \n",
       "min    417428.000000   13652.000000  3.700000e+04      13.500000   \n",
       "25%    476138.250000   47349.000000  6.536800e+04      70.000000   \n",
       "50%    533845.500000   53873.000000  7.022500e+04      78.120000   \n",
       "75%    593554.750000   60389.500000  7.774600e+04      84.470000   \n",
       "max    671033.000000  990572.000000  1.628992e+06      95.000000   \n",
       "\n",
       "                  品牌          汽车销售商             车厂           出生日期      货款日期  \\\n",
       "count  101014.000000  101014.000000  101014.000000  101014.000000  101014.0   \n",
       "mean       71.658275   19331.470598      70.801344    1981.919160    2018.0   \n",
       "std        67.305972    3517.021764      22.212900       9.540514       0.0   \n",
       "min         1.000000   10524.000000      45.000000    1949.000000    2018.0   \n",
       "25%        15.000000   16103.000000      48.000000    1975.000000    2018.0   \n",
       "50%        63.000000   18520.000000      86.000000    1983.000000    2018.0   \n",
       "75%       135.000000   22882.000000      86.000000    1990.000000    2018.0   \n",
       "max       261.000000   24803.000000     156.000000    2000.000000    2018.0   \n",
       "\n",
       "                  地区  \n",
       "count  101014.000000  \n",
       "mean        7.100907  \n",
       "std         4.333711  \n",
       "min         1.000000  \n",
       "25%         4.000000  \n",
       "50%         6.000000  \n",
       "75%         9.000000  \n",
       "max        22.000000  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_act.describe().iloc[:,:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba2593ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>对接员工编号</th>\n",
       "      <th>是否填写手机号</th>\n",
       "      <th>是否填写身份证</th>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <th>是否填写护照</th>\n",
       "      <th>信用评分</th>\n",
       "      <th>主账户贷款次数</th>\n",
       "      <th>主账户有效贷款次数</th>\n",
       "      <th>主账户中尚未还清有效贷款</th>\n",
       "      <th>主账户中已批准的贷款</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.0</td>\n",
       "      <td>101014.0</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1545.799038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025234</td>\n",
       "      <td>0.002703</td>\n",
       "      <td>576.850140</td>\n",
       "      <td>4.871701</td>\n",
       "      <td>2.072841</td>\n",
       "      <td>3.335970e+05</td>\n",
       "      <td>4.397758e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>976.714692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156836</td>\n",
       "      <td>0.051916</td>\n",
       "      <td>251.367119</td>\n",
       "      <td>6.593377</td>\n",
       "      <td>2.324305</td>\n",
       "      <td>1.334764e+06</td>\n",
       "      <td>3.533440e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.678296e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>705.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.860250e+03</td>\n",
       "      <td>1.399000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>679.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.469450e+04</td>\n",
       "      <td>6.200000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2356.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.977935e+05</td>\n",
       "      <td>3.047795e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3795.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>9.652492e+07</td>\n",
       "      <td>1.000000e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              对接员工编号   是否填写手机号   是否填写身份证        是否出具驾驶证         是否填写护照  \\\n",
       "count  101014.000000  101014.0  101014.0  101014.000000  101014.000000   \n",
       "mean     1545.799038       1.0       1.0       0.025234       0.002703   \n",
       "std       976.714692       0.0       0.0       0.156836       0.051916   \n",
       "min         1.000000       1.0       1.0       0.000000       0.000000   \n",
       "25%       705.000000       1.0       1.0       0.000000       0.000000   \n",
       "50%      1436.000000       1.0       1.0       0.000000       0.000000   \n",
       "75%      2356.000000       1.0       1.0       0.000000       0.000000   \n",
       "max      3795.000000       1.0       1.0       1.000000       1.000000   \n",
       "\n",
       "                信用评分        主账户贷款次数      主账户有效贷款次数  主账户中尚未还清有效贷款    主账户中已批准的贷款  \n",
       "count  101014.000000  101014.000000  101014.000000  1.010140e+05  1.010140e+05  \n",
       "mean      576.850140       4.871701       2.072841  3.335970e+05  4.397758e+05  \n",
       "std       251.367119       6.593377       2.324305  1.334764e+06  3.533440e+06  \n",
       "min         0.000000       0.000000       0.000000 -6.678296e+06  0.000000e+00  \n",
       "25%       470.000000       1.000000       1.000000  1.860250e+03  1.399000e+04  \n",
       "50%       679.000000       3.000000       1.000000  3.469450e+04  6.200000e+04  \n",
       "75%       738.000000       6.000000       3.000000  1.977935e+05  3.047795e+05  \n",
       "max       890.000000     453.000000     144.000000  9.652492e+07  1.000000e+09  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_act.describe().iloc[:,10:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "36db4603",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>主账户中已发放贷款</th>\n",
       "      <th>次账户贷款次数</th>\n",
       "      <th>次账户有效贷款次数</th>\n",
       "      <th>次账户中尚未还清有效贷款</th>\n",
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       "      <th>主账户每月还款</th>\n",
       "      <th>次账户每月还款</th>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <th>近六个月违约次数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.390042e+05</td>\n",
       "      <td>0.117687</td>\n",
       "      <td>0.054745</td>\n",
       "      <td>1.103999e+04</td>\n",
       "      <td>1.481056e+04</td>\n",
       "      <td>1.458024e+04</td>\n",
       "      <td>2.598760e+04</td>\n",
       "      <td>5.958520e+02</td>\n",
       "      <td>0.761330</td>\n",
       "      <td>0.189716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.538126e+06</td>\n",
       "      <td>0.882889</td>\n",
       "      <td>0.440440</td>\n",
       "      <td>2.370447e+05</td>\n",
       "      <td>2.554687e+05</td>\n",
       "      <td>2.546267e+05</td>\n",
       "      <td>2.135510e+05</td>\n",
       "      <td>1.833830e+04</td>\n",
       "      <td>1.235142</td>\n",
       "      <td>0.518765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.746470e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.281950e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.945000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.017425e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.295750e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3.603285e+07</td>\n",
       "      <td>2.688820e+07</td>\n",
       "      <td>2.688820e+07</td>\n",
       "      <td>2.564281e+07</td>\n",
       "      <td>3.246710e+06</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          主账户中已发放贷款        次账户贷款次数      次账户有效贷款次数  次账户中尚未还清有效贷款     次账户中已批准贷款  \\\n",
       "count  1.010140e+05  101014.000000  101014.000000  1.010140e+05  1.010140e+05   \n",
       "mean   4.390042e+05       0.117687       0.054745  1.103999e+04  1.481056e+04   \n",
       "std    3.538126e+06       0.882889       0.440440  2.370447e+05  2.554687e+05   \n",
       "min    0.000000e+00       0.000000       0.000000 -5.746470e+05  0.000000e+00   \n",
       "25%    1.281950e+04       0.000000       0.000000  0.000000e+00  0.000000e+00   \n",
       "50%    6.000000e+04       0.000000       0.000000  0.000000e+00  0.000000e+00   \n",
       "75%    3.017425e+05       0.000000       0.000000  0.000000e+00  0.000000e+00   \n",
       "max    1.000000e+09      52.000000      36.000000  3.603285e+07  2.688820e+07   \n",
       "\n",
       "          次账户中已发放贷款       主账户每月还款       次账户每月还款      近六个月新贷款次数       近六个月违约次数  \n",
       "count  1.010140e+05  1.010140e+05  1.010140e+05  101014.000000  101014.000000  \n",
       "mean   1.458024e+04  2.598760e+04  5.958520e+02       0.761330       0.189716  \n",
       "std    2.546267e+05  2.135510e+05  1.833830e+04       1.235142       0.518765  \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00       0.000000       0.000000  \n",
       "25%    0.000000e+00  0.000000e+00  0.000000e+00       0.000000       0.000000  \n",
       "50%    0.000000e+00  1.945000e+03  0.000000e+00       0.000000       0.000000  \n",
       "75%    0.000000e+00  8.295750e+03  0.000000e+00       1.000000       0.000000  \n",
       "max    2.688820e+07  2.564281e+07  3.246710e+06      35.000000      20.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_act.describe().iloc[:,20:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f8f88987",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均贷款期限</th>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <th>贷款查询次数</th>\n",
       "      <th>是否违约</th>\n",
       "      <th>贷款与资产比</th>\n",
       "      <th>贷款总次数</th>\n",
       "      <th>主账户无效贷款次数</th>\n",
       "      <th>次账户无效贷款次数</th>\n",
       "      <th>无效贷款总次数</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.00000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>1.010140e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.931861</td>\n",
       "      <td>26.079969</td>\n",
       "      <td>0.329608</td>\n",
       "      <td>0.165462</td>\n",
       "      <td>0.735170</td>\n",
       "      <td>4.989388</td>\n",
       "      <td>2.79886</td>\n",
       "      <td>0.062942</td>\n",
       "      <td>2.861801</td>\n",
       "      <td>3.446370e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.950019</td>\n",
       "      <td>23.427317</td>\n",
       "      <td>0.898052</td>\n",
       "      <td>0.371599</td>\n",
       "      <td>0.110548</td>\n",
       "      <td>6.664515</td>\n",
       "      <td>5.32659</td>\n",
       "      <td>0.578744</td>\n",
       "      <td>5.365879</td>\n",
       "      <td>1.358465e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.124130</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.678296e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.678678</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.691250e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755254</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.686050e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.819912</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.074430e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>117.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.937293</td>\n",
       "      <td>453.000000</td>\n",
       "      <td>451.00000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>451.000000</td>\n",
       "      <td>9.652492e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              平均贷款期限      第一次贷款距今时间         贷款查询次数           是否违约  \\\n",
       "count  101014.000000  101014.000000  101014.000000  101014.000000   \n",
       "mean       15.931861      26.079969       0.329608       0.165462   \n",
       "std        15.950019      23.427317       0.898052       0.371599   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         5.000000       8.000000       0.000000       0.000000   \n",
       "50%        13.000000      19.000000       0.000000       0.000000   \n",
       "75%        21.000000      37.000000       0.000000       0.000000   \n",
       "max       117.000000     117.000000      28.000000       1.000000   \n",
       "\n",
       "              贷款与资产比          贷款总次数     主账户无效贷款次数      次账户无效贷款次数  \\\n",
       "count  101014.000000  101014.000000  101014.00000  101014.000000   \n",
       "mean        0.735170       4.989388       2.79886       0.062942   \n",
       "std         0.110548       6.664515       5.32659       0.578744   \n",
       "min         0.124130       1.000000       0.00000       0.000000   \n",
       "25%         0.678678       1.000000       0.00000       0.000000   \n",
       "50%         0.755254       3.000000       1.00000       0.000000   \n",
       "75%         0.819912       6.000000       3.00000       0.000000   \n",
       "max         0.937293     453.000000     451.00000      42.000000   \n",
       "\n",
       "             无效贷款总次数    尚未还清有效贷款总额  \n",
       "count  101014.000000  1.010140e+05  \n",
       "mean        2.861801  3.446370e+05  \n",
       "std         5.365879  1.358465e+06  \n",
       "min         0.000000 -6.678296e+06  \n",
       "25%         0.000000  2.691250e+03  \n",
       "50%         1.000000  3.686050e+04  \n",
       "75%         3.000000  2.074430e+05  \n",
       "max       451.000000  9.652492e+07  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_act.describe().iloc[:,30:40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b29f4e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>已批准贷款总额</th>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已还贷款比例</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>次账户还款期数</th>\n",
       "      <th>贷款与已批准贷款比例</th>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <th>工作类型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>101014.0000</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>1.010140e+05</td>\n",
       "      <td>101014.000000</td>\n",
       "      <td>101014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.545864e+05</td>\n",
       "      <td>4.535844e+05</td>\n",
       "      <td>2.658345e+04</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.000341e+05</td>\n",
       "      <td>5.789012e+03</td>\n",
       "      <td>1.093500e+03</td>\n",
       "      <td>1.867784</td>\n",
       "      <td>0.484804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.544442e+06</td>\n",
       "      <td>3.549034e+06</td>\n",
       "      <td>2.145492e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.199059e+06</td>\n",
       "      <td>1.497526e+05</td>\n",
       "      <td>1.604830e+05</td>\n",
       "      <td>0.932030</td>\n",
       "      <td>0.540795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-110000.3300</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>1.450000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.500000e+04</td>\n",
       "      <td>6.363200e+04</td>\n",
       "      <td>2.034000e+03</td>\n",
       "      <td>1.2500</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.239882e+05</td>\n",
       "      <td>3.200000e+05</td>\n",
       "      <td>8.602250e+03</td>\n",
       "      <td>2.0375</td>\n",
       "      <td>1.377450e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>2.564281e+07</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>1.980000e+07</td>\n",
       "      <td>5.000000e+07</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            已批准贷款总额       已发放贷款总额        每月还款总额    贷款与已还贷款比例       主账户还款期数  \\\n",
       "count  1.010140e+05  1.010140e+05  1.010140e+05  101014.0000  1.010140e+05   \n",
       "mean   4.545864e+05  4.535844e+05  2.658345e+04          inf  1.000341e+05   \n",
       "std    3.544442e+06  3.549034e+06  2.145492e+05          NaN  3.199059e+06   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00 -110000.3300  0.000000e+00   \n",
       "25%    1.500000e+04  1.450000e+04  0.000000e+00       1.0000  3.000000e+00   \n",
       "50%    6.500000e+04  6.363200e+04  2.034000e+03       1.2500  2.400000e+01   \n",
       "75%    3.239882e+05  3.200000e+05  8.602250e+03       2.0375  1.377450e+04   \n",
       "max    1.000000e+09  1.000000e+09  2.564281e+07          inf  1.000000e+09   \n",
       "\n",
       "            次账户还款期数    贷款与已批准贷款比例  总贷款次数与总有效贷款次数比           工作类型  \n",
       "count  1.010140e+05  1.010140e+05   101014.000000  101014.000000  \n",
       "mean   5.789012e+03  1.093500e+03        1.867784       0.484804  \n",
       "std    1.497526e+05  1.604830e+05        0.932030       0.540795  \n",
       "min    0.000000e+00  0.000000e+00        1.000000       0.000000  \n",
       "25%    0.000000e+00  1.000000e+00        1.250000       0.000000  \n",
       "50%    0.000000e+00  1.000000e+00        1.670000       0.000000  \n",
       "75%    0.000000e+00  1.000000e+00        2.000000       1.000000  \n",
       "max    1.980000e+07  5.000000e+07       18.000000       2.000000  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_act.describe().iloc[:,40:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "3974a324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "客户编号              0\n",
       "已发货款              0\n",
       "资产成本              0\n",
       "贷款与资产比例           0\n",
       "品牌                0\n",
       "汽车销售商             0\n",
       "车厂                0\n",
       "出生日期              0\n",
       "货款日期              0\n",
       "地区                0\n",
       "对接员工编号            0\n",
       "是否填写手机号           0\n",
       "是否填写身份证           0\n",
       "是否出具驾驶证           0\n",
       "是否填写护照            0\n",
       "信用评分              0\n",
       "主账户贷款次数           0\n",
       "主账户有效贷款次数         0\n",
       "主账户中尚未还清有效贷款      0\n",
       "主账户中已批准的贷款        0\n",
       "主账户中已发放贷款         0\n",
       "次账户贷款次数           0\n",
       "次账户有效贷款次数         0\n",
       "次账户中尚未还清有效贷款      0\n",
       "次账户中已批准贷款         0\n",
       "次账户中已发放贷款         0\n",
       "主账户每月还款           0\n",
       "次账户每月还款           0\n",
       "近六个月新贷款次数         0\n",
       "近六个月违约次数          0\n",
       "平均贷款期限            0\n",
       "第一次贷款距今时间         0\n",
       "贷款查询次数            0\n",
       "是否违约              0\n",
       "贷款与资产比            0\n",
       "贷款总次数             0\n",
       "主账户无效贷款次数         0\n",
       "次账户无效贷款次数         0\n",
       "无效贷款总次数           0\n",
       "尚未还清有效贷款总额        0\n",
       "已批准贷款总额           0\n",
       "已发放贷款总额           0\n",
       "每月还款总额            0\n",
       "贷款与已还贷款比例         0\n",
       "主账户还款期数           0\n",
       "次账户还款期数           0\n",
       "贷款与已批准贷款比例        0\n",
       "总贷款次数与总有效贷款次数比    0\n",
       "工作类型              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看是否存在缺失值\n",
    "user_act.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "bbc995c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_act = user_act[user_act['信用评分']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "7ef781f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['货款日期', '是否填写手机号', '是否填写身份证']"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_value_feature = [col for col in user_act.columns if user_act[col].nunique()==1]\n",
    "one_value_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "fb7e6c76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100025 entries, 1 to 199716\n",
      "Data columns (total 46 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   客户编号            100025 non-null  int64  \n",
      " 1   已发货款            100025 non-null  int64  \n",
      " 2   资产成本            100025 non-null  int64  \n",
      " 3   贷款与资产比例         100025 non-null  float64\n",
      " 4   品牌              100025 non-null  int64  \n",
      " 5   汽车销售商           100025 non-null  int64  \n",
      " 6   车厂              100025 non-null  int64  \n",
      " 7   出生日期            100025 non-null  int64  \n",
      " 8   地区              100025 non-null  int64  \n",
      " 9   对接员工编号          100025 non-null  int64  \n",
      " 10  是否出具驾驶证         100025 non-null  int64  \n",
      " 11  是否填写护照          100025 non-null  int64  \n",
      " 12  信用评分            100025 non-null  int64  \n",
      " 13  主账户贷款次数         100025 non-null  int64  \n",
      " 14  主账户有效贷款次数       100025 non-null  int64  \n",
      " 15  主账户中尚未还清有效贷款    100025 non-null  int64  \n",
      " 16  主账户中已批准的贷款      100025 non-null  int64  \n",
      " 17  主账户中已发放贷款       100025 non-null  int64  \n",
      " 18  次账户贷款次数         100025 non-null  int64  \n",
      " 19  次账户有效贷款次数       100025 non-null  int64  \n",
      " 20  次账户中尚未还清有效贷款    100025 non-null  int64  \n",
      " 21  次账户中已批准贷款       100025 non-null  int64  \n",
      " 22  次账户中已发放贷款       100025 non-null  int64  \n",
      " 23  主账户每月还款         100025 non-null  int64  \n",
      " 24  次账户每月还款         100025 non-null  int64  \n",
      " 25  近六个月新贷款次数       100025 non-null  int64  \n",
      " 26  近六个月违约次数        100025 non-null  int64  \n",
      " 27  平均贷款期限          100025 non-null  int64  \n",
      " 28  第一次贷款距今时间       100025 non-null  int64  \n",
      " 29  贷款查询次数          100025 non-null  int64  \n",
      " 30  是否违约            100025 non-null  int64  \n",
      " 31  贷款与资产比          100025 non-null  float64\n",
      " 32  贷款总次数           100025 non-null  int64  \n",
      " 33  主账户无效贷款次数       100025 non-null  int64  \n",
      " 34  次账户无效贷款次数       100025 non-null  int64  \n",
      " 35  无效贷款总次数         100025 non-null  int64  \n",
      " 36  尚未还清有效贷款总额      100025 non-null  int64  \n",
      " 37  已批准贷款总额         100025 non-null  int64  \n",
      " 38  已发放贷款总额         100025 non-null  int64  \n",
      " 39  每月还款总额          100025 non-null  int64  \n",
      " 40  贷款与已还贷款比例       100025 non-null  float64\n",
      " 41  主账户还款期数         100025 non-null  int64  \n",
      " 42  次账户还款期数         100025 non-null  int64  \n",
      " 43  贷款与已批准贷款比例      100025 non-null  float64\n",
      " 44  总贷款次数与总有效贷款次数比  100025 non-null  float64\n",
      " 45  工作类型            100025 non-null  int64  \n",
      "dtypes: float64(5), int64(41)\n",
      "memory usage: 35.9 MB\n"
     ]
    }
   ],
   "source": [
    "user_act_a = user_act.copy()\n",
    "user_act_a = user_act_a.drop(columns=one_value_feature,axis=1)\n",
    "user_act_a.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "45731d73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100025 entries, 1 to 199716\n",
      "Data columns (total 42 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   客户编号            100025 non-null  int64  \n",
      " 1   已发货款            100025 non-null  int64  \n",
      " 2   资产成本            100025 non-null  int64  \n",
      " 3   贷款与资产比例         100025 non-null  float64\n",
      " 4   出生日期            100025 non-null  int64  \n",
      " 5   地区              100025 non-null  int64  \n",
      " 6   是否出具驾驶证         100025 non-null  int64  \n",
      " 7   是否填写护照          100025 non-null  int64  \n",
      " 8   信用评分            100025 non-null  int64  \n",
      " 9   主账户贷款次数         100025 non-null  int64  \n",
      " 10  主账户有效贷款次数       100025 non-null  int64  \n",
      " 11  主账户中尚未还清有效贷款    100025 non-null  int64  \n",
      " 12  主账户中已批准的贷款      100025 non-null  int64  \n",
      " 13  主账户中已发放贷款       100025 non-null  int64  \n",
      " 14  次账户贷款次数         100025 non-null  int64  \n",
      " 15  次账户有效贷款次数       100025 non-null  int64  \n",
      " 16  次账户中尚未还清有效贷款    100025 non-null  int64  \n",
      " 17  次账户中已批准贷款       100025 non-null  int64  \n",
      " 18  次账户中已发放贷款       100025 non-null  int64  \n",
      " 19  主账户每月还款         100025 non-null  int64  \n",
      " 20  次账户每月还款         100025 non-null  int64  \n",
      " 21  近六个月新贷款次数       100025 non-null  int64  \n",
      " 22  近六个月违约次数        100025 non-null  int64  \n",
      " 23  平均贷款期限          100025 non-null  int64  \n",
      " 24  第一次贷款距今时间       100025 non-null  int64  \n",
      " 25  贷款查询次数          100025 non-null  int64  \n",
      " 26  是否违约            100025 non-null  int64  \n",
      " 27  贷款与资产比          100025 non-null  float64\n",
      " 28  贷款总次数           100025 non-null  int64  \n",
      " 29  主账户无效贷款次数       100025 non-null  int64  \n",
      " 30  次账户无效贷款次数       100025 non-null  int64  \n",
      " 31  无效贷款总次数         100025 non-null  int64  \n",
      " 32  尚未还清有效贷款总额      100025 non-null  int64  \n",
      " 33  已批准贷款总额         100025 non-null  int64  \n",
      " 34  已发放贷款总额         100025 non-null  int64  \n",
      " 35  每月还款总额          100025 non-null  int64  \n",
      " 36  贷款与已还贷款比例       100025 non-null  float64\n",
      " 37  主账户还款期数         100025 non-null  int64  \n",
      " 38  次账户还款期数         100025 non-null  int64  \n",
      " 39  贷款与已批准贷款比例      100025 non-null  float64\n",
      " 40  总贷款次数与总有效贷款次数比  100025 non-null  float64\n",
      " 41  工作类型            100025 non-null  int64  \n",
      "dtypes: float64(5), int64(37)\n",
      "memory usage: 32.8 MB\n"
     ]
    }
   ],
   "source": [
    "user_act_a = user_act_a.drop(columns=['对接员工编号','品牌','汽车销售商','车厂'],axis=1)\n",
    "user_act_a.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "fd3656b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100025 entries, 1 to 199716\n",
      "Data columns (total 41 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   客户编号            100025 non-null  int64  \n",
      " 1   资产成本            100025 non-null  int64  \n",
      " 2   贷款与资产比例         100025 non-null  float64\n",
      " 3   出生日期            100025 non-null  int64  \n",
      " 4   地区              100025 non-null  int64  \n",
      " 5   是否出具驾驶证         100025 non-null  int64  \n",
      " 6   是否填写护照          100025 non-null  int64  \n",
      " 7   信用评分            100025 non-null  int64  \n",
      " 8   主账户贷款次数         100025 non-null  int64  \n",
      " 9   主账户有效贷款次数       100025 non-null  int64  \n",
      " 10  主账户中尚未还清有效贷款    100025 non-null  int64  \n",
      " 11  主账户中已批准的贷款      100025 non-null  int64  \n",
      " 12  主账户中已发放贷款       100025 non-null  int64  \n",
      " 13  次账户贷款次数         100025 non-null  int64  \n",
      " 14  次账户有效贷款次数       100025 non-null  int64  \n",
      " 15  次账户中尚未还清有效贷款    100025 non-null  int64  \n",
      " 16  次账户中已批准贷款       100025 non-null  int64  \n",
      " 17  次账户中已发放贷款       100025 non-null  int64  \n",
      " 18  主账户每月还款         100025 non-null  int64  \n",
      " 19  次账户每月还款         100025 non-null  int64  \n",
      " 20  近六个月新贷款次数       100025 non-null  int64  \n",
      " 21  近六个月违约次数        100025 non-null  int64  \n",
      " 22  平均贷款期限          100025 non-null  int64  \n",
      " 23  第一次贷款距今时间       100025 non-null  int64  \n",
      " 24  贷款查询次数          100025 non-null  int64  \n",
      " 25  是否违约            100025 non-null  int64  \n",
      " 26  贷款与资产比          100025 non-null  float64\n",
      " 27  贷款总次数           100025 non-null  int64  \n",
      " 28  主账户无效贷款次数       100025 non-null  int64  \n",
      " 29  次账户无效贷款次数       100025 non-null  int64  \n",
      " 30  无效贷款总次数         100025 non-null  int64  \n",
      " 31  尚未还清有效贷款总额      100025 non-null  int64  \n",
      " 32  已批准贷款总额         100025 non-null  int64  \n",
      " 33  已发放贷款总额         100025 non-null  int64  \n",
      " 34  每月还款总额          100025 non-null  int64  \n",
      " 35  贷款与已还贷款比例       100025 non-null  float64\n",
      " 36  主账户还款期数         100025 non-null  int64  \n",
      " 37  次账户还款期数         100025 non-null  int64  \n",
      " 38  贷款与已批准贷款比例      100025 non-null  float64\n",
      " 39  总贷款次数与总有效贷款次数比  100025 non-null  float64\n",
      " 40  工作类型            100025 non-null  int64  \n",
      "dtypes: float64(5), int64(36)\n",
      "memory usage: 32.1 MB\n"
     ]
    }
   ],
   "source": [
    "user_act_a = user_act_a.drop(columns=['已发货款'],axis=1)\n",
    "user_act_a.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c25c43a2",
   "metadata": {},
   "source": [
    "### 衍生字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c38e5a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#有效贷款总次数=主账户有效贷款次数+次账户有效贷款次数\n",
    "user_act_b = user_act_a.copy()\n",
    "user_act_b['有效贷款总次数'] = user_act_b['主账户有效贷款次数']+user_act_b['次账户有效贷款次数']\n",
    "#贷款通过率=有效贷款总次数/贷款总次数\n",
    "user_act_b['贷款通过率'] = user_act_b['有效贷款总次数']/user_act_b['贷款总次数']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "899a54a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1         2.343846e+06\n",
       "3         1.905287e+04\n",
       "4         7.188286e+05\n",
       "5         2.205642e+04\n",
       "6         1.154741e+05\n",
       "              ...     \n",
       "199709    9.745027e+04\n",
       "199711    2.487053e+05\n",
       "199712    8.705673e+05\n",
       "199714    1.406028e+06\n",
       "199716    1.214401e+05\n",
       "Name: 用户资产, Length: 100025, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户资产=贷款与已批准贷款比例*已批准贷款总额/贷款与资产比\n",
    "user_act_b['用户资产'] = user_act_b['贷款与已批准贷款比例']*user_act_b['已批准贷款总额']/user_act_b['贷款与资产比']\n",
    "user_act_b['用户资产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "960b01b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#还款总期数=主账户还款期数+次账户还款期数\n",
    "user_act_b['还款总期数'] = user_act_b['主账户还款期数']+user_act_b['次账户还款期数']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "aadb98ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>客户编号</th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比例</th>\n",
       "      <th>出生日期</th>\n",
       "      <th>地区</th>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <th>是否填写护照</th>\n",
       "      <th>信用评分</th>\n",
       "      <th>主账户贷款次数</th>\n",
       "      <th>主账户有效贷款次数</th>\n",
       "      <th>...</th>\n",
       "      <th>主账户还款期数</th>\n",
       "      <th>次账户还款期数</th>\n",
       "      <th>贷款与已批准贷款比例</th>\n",
       "      <th>总贷款次数与总有效贷款次数比</th>\n",
       "      <th>工作类型</th>\n",
       "      <th>有效贷款总次数</th>\n",
       "      <th>贷款通过率</th>\n",
       "      <th>用户资产</th>\n",
       "      <th>还款总期数</th>\n",
       "      <th>年龄区间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>519488</td>\n",
       "      <td>65325</td>\n",
       "      <td>89.55</td>\n",
       "      <td>1967</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>2.343846e+06</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>648134</td>\n",
       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>1995</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>763</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>13813</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.905287e+04</td>\n",
       "      <td>13813</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>458210</td>\n",
       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>1974</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>379</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>7.188286e+05</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>616513</td>\n",
       "      <td>79605</td>\n",
       "      <td>82.91</td>\n",
       "      <td>1993</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>749</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.205642e+04</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>453368</td>\n",
       "      <td>62371</td>\n",
       "      <td>89.79</td>\n",
       "      <td>1971</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.154741e+05</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199709</th>\n",
       "      <td>470368</td>\n",
       "      <td>63387</td>\n",
       "      <td>88.35</td>\n",
       "      <td>1985</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.745027e+04</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199711</th>\n",
       "      <td>432468</td>\n",
       "      <td>73701</td>\n",
       "      <td>88.19</td>\n",
       "      <td>1976</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>726</td>\n",
       "      <td>22</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>2.487053e+05</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199712</th>\n",
       "      <td>436304</td>\n",
       "      <td>60424</td>\n",
       "      <td>62.89</td>\n",
       "      <td>1986</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>753</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>525000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.705673e+05</td>\n",
       "      <td>525000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199714</th>\n",
       "      <td>466468</td>\n",
       "      <td>62710</td>\n",
       "      <td>89.30</td>\n",
       "      <td>1973</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>771</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>487</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.406028e+06</td>\n",
       "      <td>487</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199716</th>\n",
       "      <td>638308</td>\n",
       "      <td>72000</td>\n",
       "      <td>89.58</td>\n",
       "      <td>1970</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>708</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>106508</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.214401e+05</td>\n",
       "      <td>106508</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100025 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          客户编号   资产成本  贷款与资产比例  出生日期  地区  是否出具驾驶证  是否填写护照  信用评分  主账户贷款次数  \\\n",
       "1       519488  65325    89.55  1967   6        0       0   300        7   \n",
       "3       648134  99750    73.68  1995   8        0       0   763        1   \n",
       "4       458210  65450    79.45  1974  17        0       0   379        8   \n",
       "5       616513  79605    82.91  1993   3        0       0   749        2   \n",
       "6       453368  62371    89.79  1971   6        0       0   300        3   \n",
       "...        ...    ...      ...   ...  ..      ...     ...   ...      ...   \n",
       "199709  470368  63387    88.35  1985  15        0       0   300        6   \n",
       "199711  432468  73701    88.19  1976   8        0       0   726       22   \n",
       "199712  436304  60424    62.89  1986   3        0       0   753        2   \n",
       "199714  466468  62710    89.30  1973   6        0       0   771        2   \n",
       "199716  638308  72000    89.58  1970   4        0       0   708        3   \n",
       "\n",
       "        主账户有效贷款次数  ...  主账户还款期数  次账户还款期数  贷款与已批准贷款比例  总贷款次数与总有效贷款次数比  工作类型  \\\n",
       "1               2  ...       59        0        1.00            1.33     1   \n",
       "3               1  ...    13813        0        1.00            2.00     0   \n",
       "4               1  ...       42        0        1.00            1.06     1   \n",
       "5               1  ...       11        0        1.00            1.50     0   \n",
       "6               1  ...       31        0        1.00            1.33     1   \n",
       "...           ...  ...      ...      ...         ...             ...   ...   \n",
       "199709          3  ...       14        0        1.07            1.75     0   \n",
       "199711          7  ...        0        0        1.00            1.44     1   \n",
       "199712          2  ...   525000        0        1.00            3.00     0   \n",
       "199714          2  ...      487        0        1.00            3.00     1   \n",
       "199716          2  ...   106508        0        1.00            2.00     2   \n",
       "\n",
       "        有效贷款总次数     贷款通过率          用户资产   还款总期数  年龄区间  \n",
       "1             2  0.285714  2.343846e+06      59     5  \n",
       "3             1  1.000000  1.905287e+04   13813     1  \n",
       "4             1  0.062500  7.188286e+05      42     4  \n",
       "5             1  0.500000  2.205642e+04      11     2  \n",
       "6             1  0.333333  1.154741e+05      31     4  \n",
       "...         ...       ...           ...     ...   ...  \n",
       "199709        3  0.500000  9.745027e+04      14     3  \n",
       "199711        7  0.318182  2.487053e+05       0     4  \n",
       "199712        2  1.000000  8.705673e+05  525000     3  \n",
       "199714        2  1.000000  1.406028e+06     487     4  \n",
       "199716        2  0.666667  1.214401e+05  106508     4  \n",
       "\n",
       "[100025 rows x 46 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#年龄区间\n",
    "a = pd.DataFrame(2018-user_act_b['出生日期'])\n",
    "age = []\n",
    "for i in range(len(a)):\n",
    "    #大学生\n",
    "    if a.iloc[i,0]<=23:\n",
    "        age.append(1)\n",
    "    elif a.iloc[i,0]<=26:  #刚工作\n",
    "        age.append(2)\n",
    "    elif a.iloc[i,0]<=35:  #上升期\n",
    "        age.append(3)\n",
    "    elif a.iloc[i,0]<=50:  #中年\n",
    "        age.append(4)\n",
    "    else:\n",
    "        age.append(5)\n",
    "user_act_b['年龄区间'] = age\n",
    "user_act_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "d0136f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['客户编号', '资产成本', '贷款与资产比例', '出生日期', '地区', '是否出具驾驶证', '是否填写护照', '信用评分',\n",
       "       '主账户贷款次数', '主账户有效贷款次数', '主账户中尚未还清有效贷款', '主账户中已批准的贷款', '主账户中已发放贷款',\n",
       "       '次账户贷款次数', '次账户有效贷款次数', '次账户中尚未还清有效贷款', '次账户中已批准贷款', '次账户中已发放贷款',\n",
       "       '主账户每月还款', '次账户每月还款', '近六个月新贷款次数', '近六个月违约次数', '平均贷款期限', '第一次贷款距今时间',\n",
       "       '贷款查询次数', '是否违约', '贷款与资产比', '贷款总次数', '主账户无效贷款次数', '次账户无效贷款次数',\n",
       "       '无效贷款总次数', '尚未还清有效贷款总额', '已批准贷款总额', '已发放贷款总额', '每月还款总额', '贷款与已还贷款比例',\n",
       "       '主账户还款期数', '次账户还款期数', '贷款与已批准贷款比例', '总贷款次数与总有效贷款次数比', '工作类型', '有效贷款总次数',\n",
       "       '贷款通过率', '用户资产', '还款总期数', '年龄区间'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_act_b.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "4662786c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比例</th>\n",
       "      <th>地区</th>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <th>是否填写护照</th>\n",
       "      <th>信用评分</th>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <th>平均贷款期限</th>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <th>...</th>\n",
       "      <th>已发放贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已还贷款比例</th>\n",
       "      <th>贷款与已批准贷款比例</th>\n",
       "      <th>工作类型</th>\n",
       "      <th>有效贷款总次数</th>\n",
       "      <th>贷款通过率</th>\n",
       "      <th>用户资产</th>\n",
       "      <th>还款总期数</th>\n",
       "      <th>年龄区间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65325</td>\n",
       "      <td>89.55</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>2036500</td>\n",
       "      <td>34455</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>2.343846e+06</td>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>13813</td>\n",
       "      <td>0</td>\n",
       "      <td>13814.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.905287e+04</td>\n",
       "      <td>13813</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>379</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>550000</td>\n",
       "      <td>12863</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>7.188286e+05</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>79605</td>\n",
       "      <td>82.91</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>749</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>17700</td>\n",
       "      <td>1475</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.205642e+04</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62371</td>\n",
       "      <td>89.79</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>100000</td>\n",
       "      <td>3207</td>\n",
       "      <td>7.70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.154741e+05</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    资产成本  贷款与资产比例  地区  是否出具驾驶证  是否填写护照  信用评分  近六个月新贷款次数  近六个月违约次数  平均贷款期限  \\\n",
       "1  65325    89.55   6        0       0   300          0         0      27   \n",
       "3  99750    73.68   8        0       0   763          0         0      25   \n",
       "4  65450    79.45  17        0       0   379          0         1       4   \n",
       "5  79605    82.91   3        0       0   749          1         0      13   \n",
       "6  62371    89.79   6        0       0   300          0         0      17   \n",
       "\n",
       "   第一次贷款距今时间  ...  已发放贷款总额  每月还款总额  贷款与已还贷款比例  贷款与已批准贷款比例  工作类型  有效贷款总次数  \\\n",
       "1         64  ...  2036500   34455       0.99         1.0     1        2   \n",
       "3         25  ...    13813       0   13814.00         1.0     0        1   \n",
       "4         16  ...   550000   12863       1.18         1.0     1        1   \n",
       "5         24  ...    17700    1475       1.09         1.0     0        1   \n",
       "6         30  ...   100000    3207       7.70         1.0     1        1   \n",
       "\n",
       "      贷款通过率          用户资产  还款总期数  年龄区间  \n",
       "1  0.285714  2.343846e+06     59     5  \n",
       "3  1.000000  1.905287e+04  13813     1  \n",
       "4  0.062500  7.188286e+05     42     4  \n",
       "5  0.500000  2.205642e+04     11     2  \n",
       "6  0.333333  1.154741e+05     31     4  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = ['资产成本', '贷款与资产比例', '地区', '是否出具驾驶证', '是否填写护照', '信用评分','近六个月新贷款次数',\n",
    "       '近六个月违约次数', '平均贷款期限', '第一次贷款距今时间', '贷款查询次数', '贷款总次数',  '无效贷款总次数',\n",
    "       '尚未还清有效贷款总额', '已批准贷款总额', '已发放贷款总额', '每月还款总额', '贷款与已还贷款比例',\n",
    "       '贷款与已批准贷款比例', '工作类型', '有效贷款总次数','贷款通过率', '用户资产', '还款总期数', '年龄区间']\n",
    "\n",
    "user_info = user_act_b[col]\n",
    "user_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "32dbd96e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LouisLou\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\seaborn\\utils.py:95: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.draw()\n",
      "C:\\Users\\LouisLou\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\IPython\\core\\pylabtools.py:137: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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P5R4oIh1UdVZxTnLpmBuokpLIymm/MXXcZycUFxkXw8Dnb+fFy0bmxVWpU40e917BwT3pJZY3oUESvUdejQSGEBBRkaP7thSYb39aOsMeeYrMrKzivH1jjDHGGION4Jc6EekLvAT0EZGHcYr9K4GmwAT3oV6HfCAi94ujNfCKiFQs6jyNu7dBAjw83/chouNjqVQzodhxYdERXPbUzQSHh+TFVUyqwoX3D6BSUnyJ5o2vm8hH//ciOZn70OwjEBBU4HsKCPDw5Kj7iIgIL+rtG2OMMcY4c/BP1aMMswK/9E0EbgI+AzKAzkAlYI2qpgIbVNW7wO8PVAXGAy8Dl6nqnqJOUqddIxZP+hmAtbOXUqtN/WLH5eTk8P7QZ8jKyMyLO3Qgi3du/g+h0eElmnfRl3PYt2WXO0UnALKPFPieIiMiiIqMKOqtG2OMMcYYLzZFp5SparaI5BbwbwBnq+pTInJBAfGHgVtEZC4QBWwrIPVLmzZt6rJ9+/ZqAB2u6cGvH04DICsjs8CR9uDwENJ27Dku7pBXAZ766HXE1a6W93eFqpXyxZ9M3uNiI0LxhESC2l61xhhjjClBZXxu/KliBf4ppKp/ehX7iMjVQKxvnIg8CkwFNgDTRKSzqu71CbsxKSmJpKQkAGa+8Y0GhQYDEBweihSwgPXQwSwKi/v0/teO+/u+OeMKjS9uXm9ZaQfJzthJQGRlJDAEPXqowFhjjDHGGHNibIrOqREMNBeRMV5tIcAmoE9ug4hUE5FJgKrqvar6IvAmcE9RJ9iyZD01WzvTZ6o1Smbv5p1/KS7XoQNZJZr3kkeupdaZDZw/PB60jM9hM8YYY4z5u7ECv5SJyIXAaJzFtM8A2SKSBFyvqlOBZiLypBu+G/iPqg7LPV5VnwYeKuo8yybPo2VqJ3oNH0iznu1YOW0hVVISOf/Oy4qMK8yBveklmvfHF7+k+/9dTkBMVfTIoULn4Od6c9wTRcYYY4wxxtg2mQ45fn2nKW0i0g/4B5A7h+Ugzgj9DGB+QYcBL6rqRwXlvafmFRoWHUHdTk1ZN3cFGTv3F9iH4sadaPyJ5H1k3ugiz3uiguJql3hOY4wxxhSq6B+1OYWyfnrnlBW2oZ0Glan37s0K/L8BcSa0ixYyn+Wemlf8rT5IK/CNMcaYcqFMFbmZM948ZfVQ2NlXl6n37s0W2f4NuNto/q0KeGOMMcYYc3pYgW+MMcYYY8qHMj43/lSxRbbGGGOMMcaUIzaCb4wxxhhjygfbfhuwAr/ciNTS+TJmj5TOr80+ccYDJZ7z7vkPl3hOsMW7xhhjjPl7sQLfGGOMMcaUDzYHH7A5+MYYY4wxxpQrNoJvjDHGGGPKB5uDD9gIvjHGGGOMMeWKjeAbY4wxxpjywebgAzaCb4wxxhhjTLliI/ilSEQ8gKqq+rS3BQ6r6sLT07PTIzwmgupNa7Nl2QbYc/B0d8cYY4wx5Y3NwQf+BgW+iKwAtvg0N1TVRPf1iUAUoDjfSAQChwEBQlS1oxuXBGSq6k4RuR5AVV8VkSqAqOoOr3POB9K9uwGEqmpb93Vxz/UY8L6q/iYiUUCi1zGrgFTgGhFRoEJuX4C6wFEgr8AXkRuBL4FtQICqHvV6zeP2t8h/tb2eGEJcSjXWTFvEzLETixUTEhVG6rhb8AR4OHwgi0+GjiXniP/97y8fcyPxKYmsmLaQ78d9VmA/fOOiK1fgmpfuYNkPCxg87jb2b9zBqu8XMKuAPvb06uMst499xt2CBHg4ciCLT4eORXOUf/70H/Zt+tM5KCAIso8UdYkA2LVnL3cMG83bLzxZrHhjjDHGmL+Lv8MUnSxgis8jLfdFVb1EVbsClwBBwAeq2k1Vu3oV1AD9gPbu86PuA+ACoKfPOY8AE70en7ttuRKAH4HrgP+KyH6gK/AicD3wPc4NwcfASKA3sFNE4kRkOnAf8ICITBeRFm7O34BxQHPgBxGZIiL7RWQK8IP7/grVoEdrPB4Pb6aOJCq+AhVrxhcrpsklHfj5la95b+DjZOzcT0rn5n7zN+3eBk+Ah2f7Pkh0fCxxNROKHZdQrzoTH36bHWu2kLZjLz8++RFR8RWI9dPH+j1aIx4Pb7l9jHX7+MsrX/OB28c6nZsT3zCJZV/M4d3+o3m3/+hiF/f709IZ9shTZGZlFSveGGOMMebvpMyP4Lum+Px9pfcfItIKeBtnJH+FiEzFKcpfVNVDblg24G8EPBtnhN7XPK/nATij8QCo6jYRuQ24E/g/4GOcG4B5wJtAZ69jHwMuA/a559mAU8gDNAGC3Zy/iMhgVc3IPV5EflbVbn765ldyu0Ys/+pnADbMXk6NNvXZs2FHkTHz3zl2eSMqRXNgdxr+pLRrxG+T5gCwZvYyarepz64N24sVN/ejHwG4+oV/gSqbF6whODKMGm3qs9dPH1cU0sdwt4+JLVOo3701NVrXY/+WXcW9TAQEeHhy1H3ccu/IYh9jjDHGmL8BW2QL/D0K/HWA7zyKHQAi0gBnNLwZcCNwjfv6QOARYJmIXKOqP+EU0o+JyF04I/CIyNVAPPAfP+cd4fX8uBsAEakFXOGecyhwD7AWCAMaAGM4NuJ/BIjlWIFfAajhvlbFSSdDgVbAbOBVEbkEZ7pO7vkCgBw/c/lvAG4AuLjimfQKDyF9+14ADqVnEpucf3Q8qJCYxFYphMaEs2XhGgAufPRaYupUzXs9pV0jfvlwGgBZGZnE+Rl9BwgOD2X/jj1+4+JTEsnKyEKzc/5SH7cuXINm5/Du5Y+Q8ec+uj98NRIcjh4uem5/ZEREkTHGGGOMMX9XZbbAF5GGOMXrevfh+/rTwBqc0f1rVDVHRK4BUNWtwLUi0gnIHWLepKpN3WOvduPeFJFA4EI/XfjY67kHr28NVHW9iHyBMxXnMNDdKy5TVbt7HVsPeA/nRuJHYCbOVKEMYDmwTVXHiUhH4Hz3mFtxvoHAnaITiDP1Z413B1X1ZeBlgIeTB+jhA1kEhgYDEBwRgnjyfzFRUExoTAQ9Rl7Fxzc9kxf79f2vs0eOzcXv89BVBOUeGx6CuzQg/zkOZhUYt2rmEkIiQknp2pKco0dPuI/dvfr458pNZB92ZlrtXrsVCWiF5stmjDHGmP8ZNoIPlO05+GuBh3Hmpr+LMw/9MM7I+sM4U1/eAL4D9orITJzR80dEZKaI7MUp6le7+W4u4DwC3AYgIsEiEuS2/+z1+MV9PUBEQgBUdQbwDTAJuNd9vOD2pW1uclWtDbQDJgC7gFBgDvCmqj6pqpu8+pL7rzJvtN5dT9BFVY8r7v3ZvmQ9NdrUAyC+YTL7N+eftuIvxhMUQN/nb2XqmAmFTnX5Y8k6arVuAEBio2T2bN5Z7Lhzb7qY1qmd+GPJOirXqsqhtAMn3MfU529l2pgJpLl9vPg/N1OlYRLiEep3b40ePVzUJTLGGGOMKffKbIGvqodVdQ9wB848+UycueyfAB8B56hqJu5uNKra0fuBU5R7bwVT0OCuer3WHZiKs7B3HPBf9/Gse57pwOVex36Cs1tOgvvIBMYDrwKIyE3uSP/T7usbgeru+1jqpy+tReTiIi5NgVZOnk+zPh0574EBNOrVlp2rNtPlrn6FxqyeupCWl3ehatNadBzam0Hjh9GoVzu/+ZdMnkfr1E70Hj6IFj3bs3zaAuJTErngzsuKjJvz/g+07tOJ9ld2I75OIrU7N6Oh28fOPn38ffJ8mvbpSLcHBtCwV1vWTF1Ii8u7kNC0Fh2G9mbg+GE07NWOmc98xsX/uZnrv3mMLQtWo0cyT/bSGWOMMaY80JxT9yjDxGdad5kiIj2B+3F2x5mCs3f8CJ+YCsAmnOku3hoATVR1sxu3FvjDfS13+5ftOCP4R7wXs4pIf5wR+Y44C3cn4yzifUZVD4tIS+AJ4IDPOaOAAcBbPtN0cqfa9MDZDecJnGk7bwNrVFVF5DzgQ5zpO+NUtZu7yLZd7racqup/70qcKToAodHh1O7UlI1zV3Jg536/scWJyeU9RQcgLDqC+p2asnbuCtILObawuLDoCFp3bM6mIvpYq1PTQmN83T3/4WLFnaiguNqlktcYY4wpB/xtVHLaZE56+pQVtmG97ihT791bmS3wRSQZ+BY4D2eE/XagE863DmFAHPAc8BLwkap28Tn+W+BGVd1YxHkCge9V9Rz3+Q84c/7/T1V3ujERwL+Alqrat4hc3+F8o3CXV9tP7mMjcCbO2oJzcRbpfq6qb4jIg8Ai4B84u/Z4CwCmqOrogs6dW+CXNN8Cv6TEqe9b/OuswDfGGGNOuTJV5GZ+8eSpK/AvvqtMvXdvZbbABxCROFUtdP9D90egolS1eMO8RZ+zgqruK+A18d3Jxk9MqKr63WBdRDwF/VhVcXIXxgp8K/CNMcaY06BMFblW4DvK7C46AEUV925MDlAixb2bb18hrxX5j6ag4t59rcAJW3+luDfGGGOMMZT5ufGnSpldZGuMMcYYY4w5cWV6BN8YY4wxxphis33wARvBN8YYY4wxplyxEfxyonSWwkJEKd0DHpSSX3IwovXwEs8JMGLeI6WS1xbvGmOMMSXM5uADNoJvjDHGGGNMuWIj+MYYY4wxpnywOfiAjeAbY4wxxhhTrliBb4wxxhhjTCkQkddEZLaIFLpQUESeF5GLSuq8VuAbY4wxxpjyISfn1D2KICKpQICqngVUE5G6BcR1AhJU9cuSugxW4BdBRCJEpEMJ56xUxOseESmzP39sjDHGGGOK1AX40H0+FejoGyAiQcArwAYR6V1SJy43i2xFZAWwxae5oaomuq9PBKIAxbmxCQQOAwKEqGpHN+4KN24RcDZQEegiIp+4x7wAVAJmAysL6E5doIeqLnFzzlXVM71ef1BEflLVj736vw5YB5wB/Au4SUTqAMuAGqpa54QvijHGGGPM/xIt+W24CyIiNwA3eDW9rKove/0dwbHaNA1I8ZNmMLAceAK4RUSSVHXsX+1buSnwgSxgik9bYu4TVb0EQEQigW+A91X1BT95jgJHgK3Az0Br4Bf3eRBwCOcm4VvgZaCZqr7oncCdZ3XYq+mA2x4FXAfsB9qISHXgM1XdCGxR1W4iMh14B1gNXKSq94rInALe82tAQ+DrAl7n4ieGUDmlGqunLWLG2Il+20Kjw+n77D+p2qQmAHPf+p5f3vyOS8fdggR4OHwwiwlDnyX7SDZ9xgyhckoiq6b9xvRxTr7itJ05sBtNe7UjrnZVQiLD+HP1Fl7o/QAAEXHRXP3WvTzX8/68fhf3PLnH3/b9v9m1bhurf1xE68vPYc8ffwIw6aG32PH7Hyed9+q37gVy8IRGISGROJ+jh5wjh8g5sOu4a71rz17uGDaat194sqCPwxhjjDHlhFvMv1xISAYQ5j6PxP/MmZY4NwbbReRdYDTwlwv88jZFZ4rP44j3iyLSCqdQTwJWiMhUEblNRELc16sAtYAaOAX6Y0BvoAXwPNBSVbOADcCNbmwln3MEquojqvq7iHQVke+Bpu5/dwN6AJ8CHwANcEb7wbmxyBUMDAfeEpHWOHd9vlKBAOAsoFrFmvH5Ahr2aI3H4+G11JFExVegYs14v23NUzuxa+021kxfzLpZy4hvmES76y5gzitf8+agx0jfuZ+6nZvTqHsbJMDDy31HEBUfS6WaCcVum/vuFOa88R2rZyxm/oc/snfzLirVTADggvsHEBQanNfvRt3b4HGPj/bK6duWq/+4WwkICODlviOIr1+DVTMW8Vr/R3it/yPHFfcnmte7XzlZ6WTv30b2/m3kHMkiJ+v4j2R/WjrDHnmKzKwsPx+VMcYYY06JMjQHH5jPsWk5zXHqR19rgNxfvmwNbPyrlwDKV4G/DnjS57EDQEQaiMhbOCPeNwLfu8cMxLngy9wFDhFABSAGCMcZeZ/oPmbjjsqr4xBQGbhSRKbkPoC8BRKq+oOqngf85v73bGCfqv6mqr8B2zn2I7SV3dH7FsD1wPvAH8BI4D0/77cLXvO6ktrUzxdQs10jln31MwDrZy8nqU19v20H96VT44y6rJq2kJiqlVgzfRH7t+xi3cylAERUjCJjdxq12jVk6aRfnIs9exnJbeoXuw2gVruGrJ21jMi4GJZMmkNym/rUbt+II5mHSN+5L6/ftdo1ZIl7/FqvnL5tALXbNyK8QiR7tzqj6VlpB2nY9QyGfPQQ/f77TzwBnpPO69svADwBiCcAsg8f1xwQ4OHJUfcRERHu56MyxhhjzP+gicAgEXkauAyn3nzEJ+Y14BwRmQH8A6d+/cv+9lN0RKQhzvyn9e7D9/Wnce6OpgDXqGqOiFwDoKpbgWvd4n67qq4XkQVApPs8ELjETVURWOqTvgrwJnDcFBoRSVHVNSKSAHQG6onIl8DrwNnujQBAHeBH9/lOVT3HLfKfAxbjfAPxDnAH8LbPuSMGDhzY7r333hvVp0+f6N4tuua7NkHhIaRt3wvAofRMKibH+21b8sUczrnjUmq2a8SutVtJ/3MfCQ2TAKjRqi5hMRFsXriGNv3PIW3HHufYjEwq1YwnODykwLbej15HjZYpBEeE0rJvJ2q1a8S88dOY+94UPIEBVK5dlZZ9O/HeDU8z4OU78vpdWE6AM/p1pnJKNVpdejYJDZMIi45g8+K1AGxfsYkDu9P4/skPuWjU1dQ7pwUrpywoVt7ctoCgAM65LTVfvwA8odH5Ru8BIiMi8rUZY4wx5hQrQz90pappItIFOA94QlW346zx9I5JB/qV9Ln/9gU+sBZ4GLgQpwCvCnQCHsWZwhKMMwcqCtgrIkvc4x5xN6ppDLRw58HnChSRGJxpM9PcNn9bG72FM50n1KutIU7hfjvOVy7ROPPrLxKRM4B3VPUeABFpD2x2jzsoIuPd/nqAeJyRfHzy58p49913v3733XcfBFJ/eGLCJ753N4cPZOVNMQmOCEE84ret692XsXbGEhZ/OpMaZ9SlfrdWpG3bTVhMBL1GXMUHN/8XgEMHDxGYe2x4KCKeQts+v/81GnVvQ+U61fjx+c/p+dBgarVvzMR7X6Fh9zbUat+IX97+nqy0g8f3++ChY310c3q3zZswncp1quEJ9PDzW5NJblM/b+Q9Y08aIZHOdLeda7ceN+WmqLy5bWfffLHffgFIUBg5B/f6+TiMMcYYY46nqns5NuPilPnbT9FR1cOqugdnlDsbyMT5GuQT4CPgHFXNxCnWF6pqR+8HzgLabAB3f9K+wLXAQzij9q3dR1XgchG5XESCRWQ2zkj7ve5jGHAmsArY5/Zttqq+grvIFmchRd7OO6o6R1VzJ4k/CVyNM20oAOcm4Xq3P//089aPm9e1b/OufAHblqwnqU09AOIbJrNv8y6/bUGhwRzcm07SmfVJbJFCdNWKpG3bQ7/nb2XyE+PZt8XJvXXJepJbO4V0QqMk9m7eWew2gEMHs8hKcy5F1UZJRCdUpO3g87hu/HCqNkqmz+NDANjidXxV93h/bXU6NKHt4PNI6dSUKinV6PP4EM69NRXVHMQjNOrehu0rNuVdjxPNm9uvgMg4ACQwFD16yM9HYYwxxpgyQXNO3aMMKw8j+IhIT5zC/jWcqTjvquoIn7AcoJWI/OzT3sDreSQwTlVniUgo8CrwmqpOE5HRwBJVneDGnuXTBw9QDbgbZyFvbrvg7L4DcCUwqIC38SzQBrgV58YkB6fYvxsY43s+nHldP7nnvODPVZs5965+TH3yo7yAlZPnc81HDxAVH0tKl+a8eslDoORr27NhO5c8fROValUlbdsejh46TMXaVanRuh5dhl5Cl6GX8Mu7U1gxeR7Xf/Qg0fGx1O3SnJf6PAhK8dqAwKAgYqpW4oLhA6nbpTkv9nmQ6PiKNO/tvLXP7n0FgBWT5zHkoweJio+lnhuHkq9t8RezAQiJDOOeX54jKyOToNAgWlzSkVb9OnNwdzprZx2bVXWieQGuGz+cGinOFygSHIYeySzg4zPGGGOMKRtET+F+oaVBRJJxtqw8D2f7yttxpuh4cLYmisMZaX8J+EhVu/gc/y1wo/cUHbdY/wx4NfdXxUQkGOcGYpSqrvbTjwCc+fhrgMdU9bDb/g7O2oDZwCWqepOfY88GBqnqEBHph/NtwXPAT6p6log8C3yoqjN9Do113/eMEckDtvm7PqHR4dTp1JSNc1eSsXN/gW0FtR8V9ckXQUqnJmw4Ll/x2rzb1/u05+938XMW9dpfyQswYp7vepiSERRXu+ggY4wxpmwrUz/Mmfn2faessA0b/FiZeu/e/vYFPoCIxKlq/jkqx8d4gChVLbj6K2UiEqCq2X7ag3H6tjt3y05VPSQiwap6WESigXQt5MMakTygVD5I3wK/pPyd/tVZgW+MMcYUqEwVuVbgO8rFFJ2iins3JgfnB6ZOG3/Fvdt+GNjtPj/k046q+tsH3xhjjDHGeCsHA9cl4W+/yNYYY4wxxhhzTLkYwTfGGGOMMaYs7YN/OtkIvjHGGGOMMeWIjeAbY4wxxpjywUbwASvwy42DUjr/oINKaXF8JiXf35BS+kJqZOvhpZL3IdudxxhjjDGlwKboGGOMMcYYU47YCL4xxhhjjCkf1KbogI3gG2OMMcYYU67YCL4xxhhjjCkXNMd+6ApsBN8YY4wxxphyxQr8AojIhSIS6z5PEpEzTzJPc3F4RCTI57VgEbHPwBhjjDGmJOTknLpHGWbFpR8iIsBoIEFEWgC1gJtEpIX7SBCRa0VkuYhMEZEVIrLMfT5fRJ7xStcPGAE0A74Vkckisk9EJgPfAA18zn21iESKSA8R6X4q3u+pEhYTQZ2OTQiPjTrdXflb2bVnL4Nvvut0d8MYY4wxfxM2B9+/nsCvQGMgGcgBFgPnAAHAz8BR4GlVfVVEbgKyVPVNEeniHp/rQeAhYBFwhar+KSI/q+r5ItIUWJ0bKCLJwC3AW8BCYJKI/KiqWcXp9KVjbqBKSiIrp/3G1HGfnVBcZFwMg56/nRcuGwlAYuOaXP3a/xESGcbGeb/z9tVPFJivz5ghVE5JZNW035g+bqLftqjKFbjypX/x+w8LuHD4QJ678mF63XMlVVISWTd3BfXPbsbTPe/zm//yMTdSJSWRFdMWMmXcZ4RGhTFo7G14AjwcPpjF20Of4dJHrichJZH1c1dQ7+zmjO15v99cqWOGUCUlkd+n/cY0t6/ebbPf+o6bPxlJVJUKaE4O+7fvYctva/n2sfe5fOwteAICOHwwiwlDnyU0JoIrnr+dVy8bVdRHcxxPYABXvvQvAmKqkZOVjh5KB08AgTGJaM4RALLTdoDmsD8tnWGPPEVmVrH+CRhjjDH/22wXHcBG8AtyH3AYmAlcgFPYd3H/+yxVnQko8H8iMh24A7jXff5fIBtARIJUNUdVHwKqAR/4nOcTIN7r73HA/erYAbwLvOc7tcefJt3b4Anw8Hzfh4iOjyWuZkKx48KiI7j8qZsJDg/Ji7vimaFsmL+Kh5tcR7VGNanVrpHffI3cfC/3HUF0fCyVaib4batSrzpfP/wO05/7nNUzFtPp6h5IgIexfR+kafc2hEaF+c3ftHubvLgYt7+tenfkx1e/4qVBj5K2cz/d/9UPCfDwYt8RNO7ehpACcjV2+/WiV7982zpeewGTRr3Nwy1uYMnXc9m/bQ+/vj+V5r07MOvVr3lz0GOk79xP4wvOpK/PNSuudld1Z+uS9WTv34onOBxEkMAQcjL3kr1/G9n7t+X9H1RAgIcnR91HRET4CZ/HGGOMMf+brMD3ISKDgfXun/uAx4GncQr3p4Fn3dcU+LeqdnHbH3ef3+6V7lERWSIij6vqFiBLRBq45+kALFDVze7f/wL2ApNzD1bVZ4C1wCwRaVZYv2u3a8TiST8DsHb2Umq2qV/suJycHN4b+gxZGZl5cZFxMcz/6EcA0rbvoeaZ/vPVateQJZN+cfMtI7lNfb9ta2ct5Y+Fa6h5ZgOqN69DZKVoFk2aQ0r7xuzdvJPsI9l+86e0a8SiSXMAWD17GbXa1Gf2u9+zauYSp58Vo4lLimfRpDnUbt+IfZt3kX3kaAHv/fh+1WxTP1/bvq27WTNzKQAVqlUiKCSYrUvXM/fdKax12yMqRpG+K40JQ5/lkNc1i4iLZvCbd3PDJyM4+x8X++3DsWvmfAZ6NAsJDEECQ/GExhAQUw1PRKVjn0NEBFGREQXmMsYYY4yXHD11jzLMpujk9y0wH7gZ5/ok4IzIKyA4U3OgGNdOVf9PRD4HzhORAOBuYLP78hZgpNteHTgXWAUsFZE4nJuMADd2HOBb5b20adOmLtu3b68G0PGaHvz64TQAsjIyqVTACH5weAj7d+w5Lu5QRiapj15HtUbJ3Dj+AQDCK0RSs019IqIjCKsQWWDRHBweQpqb71BGJpVqxvtt6/3odcTVrkpczQSCwkKo1bYhP4+fysXDBjHjjW+4eNjAAvKHHtffuJrx9Hv0eirXrkZoZBgVa1QhPCaCqS99wSXDBjHrjW+5cNgAv7mCvN57br9Szm5K0hn1OPumXoTFRJCxaz/zP/qRpFZ1ia9XnY//76XjctRoVZewmAjWz16WL3/nf/RmyaSfWfjxDG6cOIpf359K6r9vIDTq2Oj7os9nH3d9NCcHJAA9cpCjmXtBlYDoBAgIhuzDft+HMcYYY0xhrMD34c6Rz63IDgPX+4SsAz7CKbhvFpH+QCKQIyIDgVjge9+0wD+AS9znDYFX3ddmqOoo4CIAEZkFNFHVUe6c/KdV9S0/Xb0xKSmJpKQkAGa+8Y0GhQYDEBIeirNOOL/DB7PwF/fp/a9RuXY1Xur/MAAXP3QVaX/upd1V57NiynwK2uzn8MFDefmCw0MR8fht+/z+1/KO6XZHPxKa1qT9lV2Z9fZ3CCD47++h4/obgoiHj+5/lfCYCG58536euvAeOl/fk/ZXdmXO298j7n+K29fVM5aw6IvZ/LFwDY27t6FynWqExURw0YirOLgvg3VzlufdZYXFRNBrxFV8cPN//eaPq12VGq3q0urSswkOCyE6Ppb3hjydL67+uS3y+iHiQclBDx+bY6/Zh5GAINQKfGOMMebElPHdbU4Vm6JTuKPAUVXtkvvg2Jz5RsBNqtoNeAZnuk434E4/eSoBcara1Y1Zoard3IfvCs2BwHfu82rAH8Xp6OYl66nZ2plGU7VRMns37/yLceuIjIsB4FD6wQLjtixZT3JeviT2bt7pt63TTRfRIrUTAKHR4Wxb9Qd1z2pKh8Hd6f3AYKLiorns8Rv89qNWa2ejoWpufwOCAhj83O189cR49m7ZxeYl66h7VlPaDz6Png8MJDIumtTHh/jta00/ffVu279tN1c+dxuLvpjNhrkr844NCAqg/3O3MfmJ8ezbssvvtdi1bhuTx4zntf6PMOOFLzi4L8Nv3Fav6yOBwZB9lICYqiABgOAJCrfi3hhjjDEnzQp8/3IHbRVo6W5/OUVEpgCx7jaa7YAFbpy4D3C+FfGeUN4EOB+YUNRJReRWnBuKX9ymehSzwF82eR6tUjvRa/hAmvVsx8ppC6mSkkj3Oy8rMq6gfGcNOp/923bTpGc7fp+2kMopiXS7s99xcSsmz6NFakcuGD4wL85f26/vT6Vln45cP+EBPAEevh/7GYcPZrF56XqOZB1m87IN/PjqV1zg098lk+fROrUTFw8fRIue7Vk+bQFtLz+X6k1r0W1oH/4x/kHE4+yms3XpBo5kHWbLsg3MfPVrzvPp6/LJ82iZ2pGewwfS1H3vvm3hFaNIbFqLs67pQUqHJjTt1Y7KKYkMeOVOqjWtRZehl3Dd+OE06dUu3zWb8cIXdBzSkyEfP0Tdzs05sGu/32u78JOfOPdflzpz7QOC0KOHyDm4l8CYqgRWqEZOVhpkHznumDfHFbyLkTHGGGNctg8+AKJathcJnA7u3vf/AIYCX7uj7rmvTcZZeHuZqt7ktg0AjgBzgYnAbar6o/tadeCgqu7xyrFUVZt4/S3Ap8Bu4B+qelhEHgLOAwar6rqi+nx3zSs0LDqCup2asm7uCjJ2+i8uAU4krkGnpmyYu7LQuNDoCFI6NTkuzl+bt0xyCIuOoJ7bj/Qi+lFUXFh0BI07NWN9Mfpat1OT4+L8tXkLyNfy10VVqcCdUx5CjxyEEv7fYFBc7RLNZ4wxxhTC/7zY0+TgMzedssI2/LYXy9R792YF/kkSkWBVLbF5FCISoaoHTvb4u2teUSofZFAp/e82k5K/8w0ppS+kSqPAB3ho3iOlktcKfGOMMadQmSpyD/73xlNX4N/+Upl6795sis5JKsni3s130sW9McYYY4wxuazAN8YYY4wxphyxbTKNMcYYY0z5UMYXv54qNoJvjDHGGGNMOWIj+MYYY4wxpnzIsc1jwAr8cqO0drtJzC6dL3m2lMLWNKX1pZynlK7t6DMeKJW8w+Y/XOI5bWceY4wx5u/DCnxjjDHGGFM+qM3BB5uDb4wxxhhjTLliI/jGGGOMMaZ8sDn4gI3gG2OMMcYYU67YCL4xxhhjjCkX1PbBB07hCL6InPRWJCJSQUSCSqAPbUSkndffwSJSR0QGiUhFr/YaIuIRkUoiUk1EEkQkogTOnyAiHf5qHmOMMcYYYwpSogW+iNwmIq3d5z1E5BER+UBElgGv+8QGisilxUz9IvBYIeddISLTfR7bRaSZ+7pHRAKBWkAPt7BPArKABcCtwJlubDTwBVAfaALcCTQHHhGRKDcmWESiRaS7iDwqIjEiEu6+/5Ze/YoSkRFeXR0MnFHAexglIl1EZLSI3Ose+52IlMKGkoULi4mgTscmhMdGnepTm1No1569DL75rtPdDWOMMabk5Oipe5RhJT1F53PgCxHpBPQBvgSeBXaqqu+VyAFuEpEAVZ1QUEIR6es+bSAinVX1Rz9hO1S1i89xbwIH3D+bA2OBo0AA0BUYDXyqqpd6HRMPvA9McfuXCFwKxAJBwEPAXUBtoD+QANQB/gVMd9sWi0hHYKuqrnO/DbheVV8FrgSyRCTVPeVeVe0jIpFAGnAWUAWIB2oCB1Q1W0Q8AKoF7v302g2fjGDVtN+YPm5iASHQZ8wQKqckHhfn2xZVuQJXvvQvfv9hARcOH0jaij+IrlGZjdMWsfz9afR6524+umD4cXnP+ff1xKZUY+O0Rcx/9vO89rNHX83GaYvYOGUhITHhdHv2H8Q1rYkozHp78l/uK0BEXDRXPn87u9Zvo3JKIr9P+41p4yYSX686PYcP5PXBjx+XN3XMEKp4xflri61emRs+fJDQqDC2//4Hr186ipCoMC4bewueAA+HDmbx0dCx9HrkWiqnVGP1tEX86ObqPWbIcW2eAA+3z/gPe//4E4DMfQeIrBxDbPXK7F63DYAZ4z5n3cyleX28+IljOWaMnei3LTQ6nL7P/pPgiFB2rtrCpGGv57UFxFRFjx4h58CuAq/v/rR0hj3yFJlZWQXGGGOMMebvqURH8FV1A3Cequ7HKabDgXpABxE5W0Sqi0i4G5sD3AhUyz1eRAK8p+KISG/gDpzR+weAf4vI1X5OHe07gg/0ALLdcy0ExgMjcAr4F4F5QI6IhLqPEKAN8AFOcX8esBd4GecGYhAwXkQqAKuBs4HGbv87AlHAJpybgiHu8eB8O9BFRM4HZgIXun0byrEbkBigEnAf0ADYDfwTSBGRGcBmoHUBlz0VCHi57wii42OpVDPBb1Cj7m3wBHjwjvPXVqVedb5++B2mP/c5ezb9SVilaD7tM4qI+Fg6P34tgaHBx+Wt3aM1EuDJi4mpGQ9A1TPrE145ho1TFgJQL7Uj+9Zt44/pi9k8axlVGyX/5b6GRkdw6VM3E50QiyfAw4ter/UcPpCA4OPvXxu7Obzj/LVd9t9/sH3lJkY2vZ7IuBiaXXIWzXp3YParX/PWoMfJ2Lmfc/7VF0+Ah1f7jiQqvgIVa8bTsHvrfG3xDZJY8uUc3ug/mp/f+I5DGZm8d91THM48xBf3vsqb/UcfV9w37NEaj8fDa6leef20NU/txOLPZvFGv4cJjgylWtNaeW3Z+7chIkhgsO+lzRMQ4OHJUfcRERFeYIwxxhjzt6M5p+5RhpVYgS8ifdzC+ia3KQynAL4Y+A/O6HQCMEtEfhaRn4H3gMu9/v4F6OqOer8O3OIefw7QCqcw7isic0XkHK/T71HVLt4P4Fuvvl0PXA08CjwI3A70xSma3wBWAINUdRKwCMhS1XGq+o2qjgaOishXwCuAR1WzgWdwvgW4E+dbijbA48Aq4FfgawBVPaCqA3G+NRgB3IAz0l/Lff/g3IjUBJ4CtrjXqS0wDOcm6ENVnVvApe8CfAiwdvYyktvU9xtUq11Dlkz6Be84f21rZy3lj4VrqHlmAxKb1WbZe1MBOLgrjZCocA7u3H9c3mrtG7LmSyfH5lnLqHpmfTyBAXQZcx1pm3dR8/xWAGTtzSDhjLps+OE3IqtVYtX0RX+5r5qTw/ihzxIQFHjca51vvpi1Py/Pl7e2T46aber7bYurVZW57/8AwO7126nePIVf353CWrcQj6gYTcWkeJZO+hmAdbOXu31slK+tessUGp7fmus+epBud1/O8m/mUr1FHUKjwrnyjf/jyjfuIiQyLK+PNds1YtlXTo71s5eT1Ka+37aD+9KpVLsqodHhxFStxP6tu/PaEA8EBKLZR/1eX4DIiAiiIv/yshJjjDHGlEElNkVHVT8TkYXASLcpCbgZZzpLFVXNnSvREkBEKqvqTvd5VVXdlptLROrgzI0f4k5RyQKyVXUPcJGI9AI2ep2+qGHI13DWAKQCcTij8tVxiui7gXHuFBqA7TjfOEzxyRGDcxOwx/07BPcbAiBMVR8UkW7A+ap6t/eBIvIqMFRVs0SkCs7UnC+9QgLd6zYIp8g/gnMzcgZwGFjnFfsSzvoANm3aVDUpKale+/btu8amZ5OS0ZRK7gi6r+DwENJ2OF0/lJFJpZrxfttyNe3VDhEhY9sePEEB1O7RmnXfzSOxXcPj8gaFh3Bg+14AjmRkElMzgfp9O7J39RbCKkbRaeRgzhp+JVl704lvmUJi+4bsXbOVtD/3UrVh0kn19Yx+namcUo2WfTs5H0zVSnmvgVPITxz2GvXObpavr/t98vpr27d1Fw26tkIVEhomsWXhmrwcNVqlEBYTzt7Nu0jbsTdfLt+2tbOW8frlj5Cxcx9Dv3+CCjWqsPanxXz32PtEx1UgJyeHFpeezS9vfpfXxzT3eh5Kz6RicrzftiVfzKHeuS1pe3V3dq3dSub+A2z6dRX1zm2JJzQaso+U+dEFY4wxpsSV8bnxp0ppbJOp7kLVWFU95O6ec9wOOu5i1d9E5FxgA86o/mWqOg9AVdcC4wo8gTPS7i3JT0HeCGfEHKADzuh67lz6gcDHwD4/6QOBDFW9RETOA9ap6lo3/0G3/5fgTB0KBCKA/SJy0M1/XNUqIl3dPme51+IsN+Ylr7Bk4BGgLs56gRY4I/yfuq9/4xV7Y96bTkoCeGbOnDkfDKt55Zzg8FDc6fr5HD54iCB3ek1unL+2XF8++CaJzWpTo2Njks5uyh8zlpBzOP+I8JEDhwgMdWZVBYWHIh4hrkkyy96fxh/TFxObUo22d/fjaNZhdi3dyKpPZpLQuh4Nu53B/q27T6qv8yZMp3Kdavz4vDPf/+45Y/NeO/PKrqz+aTE5R7NPOG9u28Z5q9ixajNt+ndh/S8r8nKFxURw4YirGH/zM5x1/QVex4UgIhw+mJWvbcfKTWS71+3A7v1UTK7CvPf+pHJKIlIlll2rtlLn7KbH+njAK0dECOIRv21d776MSfe/zqGMTNpffwEt+51NcruGTLr/dRp0uB9PaAwSEoUeSvd7jY0xxhhTfpXWNpkjObZrjgdnYau3McAEVf1dVQ8B1wHvuzcGJ0REagELVbWb9wOYnBujqjNxRsHfxpnO8gPO/PqlflJ6b8c5Aghwd7LJxr0hUtWJwGz3fU4AvnRH5C8AqriLdXG31hyNM7ce4F6cNQAzRST3mw5UdZZXv94F3lPVIzjfYvTGmfJTkPk4awCo2iiJvZt3+g3asmQ9ya2dKTG5cf7aOt10ES1SnZHxrPRMIqtXpnqnJtTpeSb1UjsQ1yiJLk9cn5d355L1VD3TyVGpURLpm3eyf8MOopOqAFC5WS3St+wiMCyErL3pVG3bgPiWdYhOiP3Lfc116EBW3muJTWpSq21DhowfTtVGyZx3Z7/j8tb0k9dfW0TFKCpUi2Pv5p3s3byLgKAALnvuVqY8MYH9W3axdcl6klvXAyChUTJ7N/tv6/v0zcQ3TEI8QkSlaAKDg+h612U07tmOfZt30bhnW3as2JTXx21L1pPUxskR3zCZfZt3+W0LCg2mSoMaiEdIbJGCKnltABIU4vfaGmOMMab8K+kR/Bs4Nuc+d2ecHTgLXHF3gxmAM4rdNvcgVZ0mIp/jzGu/xk9eoeCbkRuAT/y0BwLqnncgzpSedTiLWSvh7I5zp5/j1gGPi8hrOAV2JDAX54blDzffhTgj94dwptNUFpFrcW4O7gDeFpE+OAtqPwACReQtIE1Vb3FzjBORz4A7VXUdzsLfA8BwYKN749IYZ4pOK5xFwf5MBH66YPhA6nVpzot9HqRySiLNe5/FlKc+ygtaMXkeQz56kKj42Lw4lHxtIh6ueO5WWl/ehd3rt1GzRR02z1pOWMUovv/nc1zw+h1Mv/vVvLzrvptPn0+GEx4fS3KXZnzSewSao5zz5BDqXtwOT2AA3930LBEJsZz79I3E1q5KxrY9ZB06zO/TFv6lvuY6sDedFqkdiYqPJWN3Gi+kPkRMfEWufvNuvvfKu3zyPG786EGi3RzPu3n9td057Sm2LttA4+5t+GjSL1zxyh1Ua1qLs4f25uyhvVn48Qyau+es26U5L/d5CBSu++iB49p2rNzEpc/8ExFh1dTfSDm7KX8sWEPD88+gerNaHMrI4vO7X8nr48rJ87nGzZHSpTmvXuLk9W3bs2E7vZ+8kQqJcfyxYDVLv5jNjhUb6f3kjQRWqoYeyUIPZRTwT+aYN8c9UWSMMcYY87dhP3QFgOTfvfIkEzlF7xCcUep+OAtiw3F2pAkAKuJMS7kK6KWqq32OD3dfv05VD/u8ditwWFVf9GmvhTPVpp074o27G84sN6SDO00oFKeo/whnS8t1wOuqepGIVAfGuttVdsJZMLsWeFtVv3BzRuDcSHTFuYEJByoAn7nv9yfgK+BKVd0pIoOBo6r6vnt8LyBSVcf79P8KYDnO7jsvuP16FGd60RM4W3LucN/jle7UJX9iP/jnM3s2zF1Jhs8iWG+h0RGkdGqCd5y/Nm+1IyOp3qkpW39ZSWYBuUNiwouM8Y1dMG95ifY1NDqCOp2asL4Y16CuT1xRbYd2phWQK5w6nZqy8bjj8rcVdMzBP/3382Tyehs2/+FCXz8ZQXG1SzynMcaYcuGkf8i0NBwYccUpm4QfMeKDMvXevZVYgQ/ONpfuDjO+7YE4Rf4R95z5J0if/DmDcot7rzZPIXvGl0kiEqiqR93nwrHdehAR8fM7AscZVvPKUvkHnZhdOrO4tgSU/MdTWh94UCn9f5enlP4vyAp8Y4wxp1CZKnIPPNj/1BX4o8aXqffurUSn6BRUuLuFa8F79v21cx7x0/a3Ku4h7xrlPleO7dBDUcW9McYYY4wxuUpjFx1jjDHGGGNOvb/fGG+pKK1ddIwxxhhjjDGngY3gG2OMMcaY8sF+6AqwAr/cCCilNS57S2klaIiW2XUp+Rwtpa6u4ECp5H2v+YNFB52gAYtGlXhOsMW7xhhjTGmwAt8YY4wxxpQLavvgAzYH3xhjjDHGmHLFRvCNMcYYY0z5YHPwARvBN8YYY4wxplyxEXxjjDHGGFM+2Ag+YCP4xhhjjDHGlCtlbgRfRERVT+r2S0QqAAdU9UjJ9qpkiEgCUEdVZ53uvhhjjDHGlDv2S7bAaSrwReQ2YJaqzhORHkBHoA7QDJgLXOMVGwhcoqofFyP1i8Bm4K4CzrsC2OHT3AA4X1UXuzH/Ae5S1Wz37yeAKao62SdXAPAx0E9Vj4pIPPCsql7uFRMF3KmqI9ymwUAWkK/AF5FRwFTgPCAdeM7Nf2FuXwrTe8wQKqdUY/W0Rfw4bmKxYwo6rtfDV7N6+iIan9+auJRqrJm2iFlj/eft+cSQ42JCosLoM+4WJMDDkQNZfDp0LDlHnLcQERfNDVOeYPe6bayZtoifCsh5kVfO3JiIuGj6vXAbb/Z7GICo+Fiu+3wUezZsByB9x14q1Kj8l/PmqlyvOuc/MJB923aXyrX9fcpMv7lufGIoiSnV+W3afD4d+5HfGICYuBj+9cI9jOh3/3HHNqmTxOapi1j8zOd+jzvryeupULdaXkxkjcq0HX0VwZFh7PxtLfNGvY8EeOg752kyNu50DgoIguzi3Tfv2rOXO4aN5u0XnixWvDHGGGNK1umaovM58LqIxAB9gJ+B24AmqnqNT2wOcJOIXE4hRKSv+7SBiHQuIGyHqnbxfgDfwnG/OBQE9BSRT0RkKnAZ8JiI/CAiM0SkpxuXCkwG4tybkD7ABhFp5z6CVTUdqCEi17vHXAlcKSLT3cdnbt8jgTTgLKAKkALUxPk2IltEPCJS2GeV6gnw8GrfkUTFV6Bizfh8AQ27t8Y3xl8bQHKb+kRWroAnMADxeHgr1Xk91k/e+j1a54tpckkHfnnlaz4Y+DgZO/dTp3PzvPg+z92KJyCAN1JHEllAXxu4Ob1jQqPD6f3UTQSFh+TFJbasw0/jJvJ2/9HMffM7so9kl0jeXOc/MJCo+Ap+r9Ffvba//7DQ3+fImT3a4fF4eDD1XmLjK5JQs6rfuIjoCP7x1G2EevU799ive48iPD6WqFr5+5p0gdMv75gzhvVn8X8/45vUh4moWpGE9g2JbZjE+olz+LbfaL7tN7rYxf3+tHSGPfIUmVlZxYo3xhhjTMk7LQW+qm4AzlPV/cBRIByoB3QQkbNFpLqIhLuxOcCNQLXc40UkQESCvP7uDdwBPAY8APxbRK72c+por+J6uohMB3oA3iPk9wOTgPvcfHOA993n96vqV+5UmyuAl4DngaZAX5yR92443yTEufluBbqIyPnATOBC95xDOXZjEQNUcs/ZANgN/BNIEZEZON9KtC7kknZZOulnANbNXk5ym/r5Amq1a4RvjL82T2AAFz9+Pfs276TVZV1Y8ZXz+obZy6nhJ29yu0b5Yua/M4X1M5cCEF4pmgO705zYsxoRViGS/Vt2FZlzuU/OnBzlk6FjOZSemRdXvWVdWg86j2s/G0GnW/vkO+Zk8wK0uKwzG+YsJzQ6PN81Kolr2+C8M/LlAWjUrglzvnK+4Fk6ezEN2jT0G5eTk8N/hz7JQa9+ex+7bdYy4v30NaF9Q9Z/+ctxMTG1E9i9ZAMAWbvSCIoKo/IZKSRd0JoLPnuATmNv9tsHfwICPDw56j4iIsKLfYwxxhhTYnL01D3KsFM+RUdE+uCM1k8DRgJhQGOcAvYc4BPgIPCliBzyOTZ3FD8QGC4iy9wcScDFwCCcIrsH8JaI/AO4R1WnucftUdVuPjnf9Hp+HU4B/l9gL86NRxAQ7PZT3NCmONOJ1gAf4BTl01V1tJunOZAJoKoHgIEiMgYYAdwAbMcp4t9z82XjjNg/5eZKcPMPc89xo6rO9XMtbwBumDBhQvLcdYuJBQ5lZFLJzyhzUHgIaTv2gleMv7YWqR3ZuXoLM1+axKC37iHpzAasnbaIQ+mZxCb7z5u+3c3hE5PYKoXQmHC2LlyDJyiATrf2YcfyjVSqUzUvvqKfnME+OSsmx3M4IzNf3Jrpi5jx7GccPpDFbXOeJSgspETyhlWIpGmfDrw3aAxnXnN+vmtUEte27VXn06NaFDXqJVG1dmJersbtmzBtwhQAMtMzSUj2P4Kf6affIeGh7N2+m6rAkfRMomsm5IsJDA/hoHsNcmM2fDWX5neksnPBGhLPacb8xyYQUzeRb/uOJvPPfbQdfRUSHI4ePui3L94iIyKKjDHGGGNM6TrlBb6qfiYiC3EKc3CK85uB2kAVVX3cbW8JICKVVXWn+7yqqm7LzSUidYAFwBB3KksWkK2qe4CLRKQXsNHr9IUOK6rqayJSA4gELgWa4KwNqAV0dc/5s6p+LyINcQr0h4EWwH4R6aWqk9zz5E37EZFXgaGqmiUiVYC9qvql16kD3esxCKfIPwI8CpwBHAbWFdDfl4GXgWf2fbDs1s2b1hAcHoKI5Is9fDCLoNBggLwYf21VG9dk3gdTydi5nx0rN1GtZYrzekQI4vGT90AWgbk5vGJCYyLoPvIqPr7pGQDOuvli5r39PTXa1Ec8npPK6euP+avIPnwUgIN70vKmEP3VvF3v7c/UMRPIOZqNZufku0b58p7EtV302Sza/V8qT9045rhcVz10HcHucaERoXnXqjiyDmTmHRsYEQp+3t/RA4cIDA06LmbxM59TpU09mtzckzUf/cTRg4fYu2ITOe613b9mKxLQirI9VmGMMcaAlvGR9VPldG6TqSISDcSq6iGc0fHjKhJ3kepvIlJfREKAWSKSN1VFVdeq6riCFqGq6iRV9S6Ok0RkivcDON/foThTfroB43EWynYDIoAMN+Z9N244zvSa6cAwdyefQFU97L6Hrm5fssSpDs/CWUjrLRl4AbgKeAhnilBvnAK/PfCLv/fnZX5y63oAJDRKZu/mXfkCti5Zj2+Mv7Y9G3cQW6MKAEcyD+cVmPENk9nvJ+/2Jeup0abecTGeoABSn7+VaWMmkOZOx6nVsQmtB59H7U5NiUupRq8x1xPfMJl9fnJuW7KeJK+c/mIABrx9D5FVKhAYGkxEpWjCoiMKPaa4eZPbNqDrvf0ZPH4YoTGRdL2zX4lf28Rmtdm5eWe+XOuXrKW+Oy0nuWFNdm7+028f/fE+tmKjJDL+yJ9/95L1VHGn7njH7Fm2kYjESix76RsAOj17M7GNkhCPkHxBa/To4WL3wxhjjDGn1+neB38k8Lr73AME+Lw+Bpigqr+7NwHXAe+7NwYnRERqAQtVtZv3A2ehbD6qmu1u1+lxDpc7Ae8tPD8BXsWZhz9ZVQ8C3VV1H87CYEQkAhiNM7ce4F6cG4OZIjLS61yzgA+BH4B3gffcrT4X4BT6vxbx9iY2T+1Ij+EDaNKzLX+u3pxXlOZaOXk+3jGrpi302zZ/wnRqtW/EtRMeoGrjmoRXjKTbAwNo2KstO1dtpvNdx+f9ffJ8mvbpmBezZupCWlzehYSmtegwtDcDxw+jYa92vHPZw7zbfzRvXPIQmqMczsikkZvznLvy97Vpn46c/8AAGvVqy+qp/hekznjmMwZ/MIzrJo7kl9e/pU7nZnnH/JW8z51zF2/3H83b/Uezbel6IipFlfi1PXNQN758eWK+c/86+RfO7tOFQQ9cQ/teHVgwdR6Jdatz+V1X+u2rv2PbPDSAmhe1Zd+qLbS8+9LjYjZ9O586l3bIi9n8w28ANLm5J8te/obsLKeQX/Sfz+j0zE1cPPlR/py/Bj2Sf0qQMcYYU+bYHHzAKVhP/UlFHgVuAv4DvKCqu0SkMtBWVSe5O8YMAO502w55HftvIM7PbjuIyM3AIVV93c9rjwG/q+qbPu3vAsNUdaPX34eBh1V1vYjcg7M41gOscPtaExiIs9tNMPCp9zaeIjJVVc8VkX44i4M/BB4H0lT1FjdmHJCI8+3AOnd0fxBwN860oqHAv4EawD9VdV5h1/TRZkO0TqembJy7koyd+/3GhEaH4xvjr81bTFQ4tTo1ZdPclRwoJG9RMb7xye45C8tZu4iYkznmZPIGxhR+jXLznui1/V0P5GsDZ4ecpp1asGLuMvbv3FesPnofO7hDO3b8vJLMAvoaHBNOtbObFhrja8CiUSfUj+IKiqtdKnmNMcacMv7nu54m6bf2OmWFbdSzk8rUe/d2ygt8EbkQGIIzmt0PZ0FsOM6odwBQEWdU/Cqgl6qu9jk+3H39utxpMF6v3QocVtUXfdpr4ewp3y73R7Byp/y4IR1U9ZCIDAA6AW+4fWyAU8B7cP4BhwBP4Eyh6Qj86O4ElHueSu5rM1T1Hq/2XkCkqo736dcVwHJgE84UnXU4c+8buee5C2ff/o+BK1V1bUHX9cGaA0rlgwwppX8eRW7sX4YcLaX/+RZU4P9VFxyNLPGcVuAbY4wpQJkqctOHXnjqCvxxX5ep9+7tdI3gB/ibN+/uJx+As8hUivMDTydwziDfX7gVEY+7DWfe33jNn/dqF5yFsB6cG4gSv2giEqiqR73O5/H6sa0if93XCvzSYwW+FfjGGGMKVKaKXCvwHafll2wLWRR7FGdf/NI4Z75f6vEu7r3+zrea0C2ui/dLPycpt7j3Ol+2z9/GGGOMMaYwZXxu/KlyuhfZGmOMMcYYY0rQaRnBN8YYY4wxpsTZCD5gI/jGGGOMMcaUKzaCX06U1mLYTVI6P3CUpMElnjOn6JCT4imlaxsvIaWS96GsxSWe888zHizxnAD/mm+Ld40xxpQcW7bosBF8Y4wxxhhjyhEbwTfGGGOMMeWDzcEHbATfGGOMMcaYcsUKfGOMMcYYY8oRm6JjjDHGGGPKB5uiA5STEXwRCRCRfD8XLI6AAo6JKCRfDRHxiEglEakmIgmFxRtjjDHGGFNWlIsCH7gFmCIiU0RksYisEZEpwA/ua4hIpIjM9jrmQxFp5JtIRKKBL4D6QBPgTqA58IiIRLkxwSISLSLdReRREYkRkXARuU1EWvrke9iNCxKRhW7bfhGZLiIbRORit227iMwRkS9F5H4Rme++nx9F5P2SvmCFCY2JoFbHJoTFRp7K05oTFB4TQf2OTYmIjTrdXSlQaEwEyfZvyRhjzCmiOXrKHmVZeZmi8wawXlU/F5FLgQaq+oiIXA6scmOOAochb/S+JXCl18D/K0AW8D4wBWdb9UTgUiAWCAIeAu4CagP9gQSgDvAvYLrbtlhEOgJbgVrA1cBFwD4gRUSGAL+rahcRGZHbJ2Ae8BYQB0Ti3GQsA6KALkVdgJ5PDCEupRprpi1i1tiJxYoJiQqjz7hbkAAPRw5k8enQsYTHRtH3pdtZ88NCuj0wkDFXjqDPPQOompLI0mkL+HrcpwX2YdCYm4+L8wR4GD3jOXb+sQOA8Q+9TpWaCVzx8PWEhoVwOD2Tnau3MH7wmJPqa1Bo8HFtHw8dS86RbAB6eR0/s4Dr4RsTEhVG6rhb8AR4OHwgi0/cfBc/MYTKKdVYPW0RMwrI5S8mIi6ay164jTf6PQxATLVK9Hn6JlSVPRt28Nb9L3HFmBtJSElk2bTfmFzItfWNi65cgeteupNlPyygz/DBjLtyFMHhofQbdS0Dw2DRgiU88uBTPPHMCFLq1WbalJ8Y+9QrfnNHRUUy7tUnCAgM4MCBgwy97v+4fGAqvS7pDkB0TBQJYdFk7c9g3bRFzBn7ud88PZ64nkop1fJiIqpU4JKXbmPtDws594EBjL/iUYIjQuk26iqCI8PYvmhtge/X1649e7lj2GjefuHJYh9jjDHG/K8qLyP42cBjIlIvt0FEQoARwE4R6QFMA1qKyI/AQOBp4FX3kYxTrLcBPsAp7s8D9gIvAztUdRAwXkQqAKuBs4HGQDWgI04hvgnnpmAIcJ6q/gC8BNyuql2AZar6CgX/JlMf4BtAgf3ALmCP+3dhUsXj4a3UkUTFVyC2Zny+gPo9WuMb0+SSDvzyytd8MPBxMnbup07n5sTVTeT7Ue8ya9znrJuxmHOu7oEnwMMTfYcTE1+RKjUT/HagZfcz88UlNkjm1y9n8nT/ETzdfwRbf9+EAMtnLOKpJkNI27ab1VPmn3RffdtSOjcHoEGP1ng8Ht50j6/o53r4i2lySQd+fuVr3vPKlxv3WiG5GvqJCY0Op89TNxEcfuzHrM4YcC6Thr/BW1c8SnS1SnS57kI8AR7+0/dBYuJjqVzAtW3mXlvvuIR6Nfjs4beZ/NxnrJixiOpNatP73gF8N/ZT+vW6moRq8dx61w14AgJIvWAw8QlVqFk7yW/+S/r15JUX3mZg3xvZ+eduOnftyLtvfEj/3tfRv/d17Nj+J3s3bOe91FFExsf6/fdV1/3MvGPi6iYyddS7/DzuC9bPWEx8k5p0vrc/s5/9jA/6PUxUQkUkKNRvn7ztT0tn2CNPkZmVVWSsMcaY/3E5euoeZVi5KPBVNQN4APAeCh4OvKuqm4Hvga7AQuACnAJ8H3CGqm4ADgGZqjoJWARkqeo4Vf1GVUcDR0XkK5xRfo+qZgPPAKNxpvA8i3Nz8DjONwa/Al979eW/IjLd6+867t9X+7yVw8BB9/kg9z3cART1s69dVnz1MwAbZi+nRpv6+QKS2zXCN2b+O1NYP3MpAOGVojmwO40Ns5axdeEaapzZgGrN6xBVKYb5k5yZTb/PXkpKmwZ+O1CvXeN8cbVb1qXF+Wfyfx89zLX/vRVPgCcvLjAkiKDwEI5k5v+l3OL21V9b7vHLi3E9fGO880W4+ZLbNWKZG7d+9nKS/OSq6SdGc5SPho7lUHpmXtzUf3/ErjVbnf5WiKRqvRosnDQHgNWzl1K7gGtbt12jfHGrZi1hw8LV1DmzIcnNU9iwYBWVa1flj6XrANi9aw8tWjXlq4nfATB7xlzatGvpN/87r09g5nSn/5UqxbJ715681+KrVqFW7WR+e/cHADbOXkain2uQ1K4hv3/1y3ExG2ctY9vCtVQ/sz5Vm9dh64I1xNZKYMfSDQDO5yVF/19QQICHJ0fdR0REeJGxxhhjTFkiIq+JyGwRGV5EXHzuVO6S8LefoiOSVyF8ilPAt3L//hL4zev1HABVPSgi5wGZwL9FZDLO9Jvc4cHtQAd3Dr+3GGCQquZWPyE43xwAhKnqgyLSDThfVe/2OfZ74HfgevfvncBNwFCfuFHA/cBmnBuHwap6rYhcWMB7vwG4YcKECck/r1tMZeBQeiaxyflHWIPCQ0jfvhf8xCS2SiE0JpytC9dwwaPXUql2VSrWSiAwLISktg2Y8e5kADIzDhY4yhwcHsq+HXuOi1s5awlPXv4QaTv3ccWo62hyTqu8uDNSO7L+p6VExMWcdF9927a4bYUdfyLn2LJwDS2vOIc0r7iKBeTyjTmUkZkvrtej1xJXuyrhlaIJqxBJ+xZ1+PHNbwHIysgkrsBrG8K+HXv9xrXq1Z7so0fJyc4h+0g2D0x/hhv37yO5Vg3CwkL59+ixAKSnZ5Bcu4bf/Hm5WjcjpkI0C+ctzmsbfF1/NqzblHetDqdnEpucv5/e19M3psFF7cg5mo1m57Dqm7l0uD2VrQvWULtzM/Rw/uvkKzLC1rcbY4wppoLmSJwGIpIKBKjqWSLyvIjUVdXVBYQ/CYSV1Ln/9gU+0A24l2MfaRUgnGPz1gNwpuF8As4CWVXd6xb+O3AK6WCcgh+ca5Khqpe4NwLrVHWtW/AfdHNcgjOyHghEAPtF5CDOXP28eRAicgtwBc5Um7pALRH5J5COM9fed0hyK9AMp8AHyBCRl4E/RWSyqh71DlbVl3GmED1z4P1lt27duIbgiBDEk29DIQ4fyCIw1PkiwDsmNCaC7iOv4uObngHgm/tfzzum852XUqFpTYLc40LDQ/Hk36wIgEMHs/LFbVm5kaOHnS5vX7uFKjUT8uIa9T6Lhe9PpUKNyifd14LaCjq+uOfo4ZXv8IFj76uwXEXFAEy6/3Via1Tm0udu4bXUEbS/M5Xg3OPCQ/GzERTgXNuC4j568HUuvOMyGndtxVO976d26/o0uu4cJk38loqVKhIa5kyBiYgMx+MpeLQ8pkI0I8fcx01X3ZHXJiKc1bENvy1YSlBoEABBEaEFXINDBBYQM+WBt+h456XU6dqCOWM/J7F1Pc68sSdLP/mJdlefUWCfjDHGmL+5LsCH7vOpOFO68xX4InIucABnkLlE/O2n6KjqZFU9V1W7qWo3nFHwN3P/VtVzgB+BK3GK5/Ei0sRt+0NVR3D8CH6QV/oRQIC71WY27g2Rqk4EZgMjgQnAl6r6Jc70nyoiEu/GjQXGArfi3JkNwSnip6rqTJw5+94+xZkGlOs1IEZVh/sW9z7m12jjLD+Ib5jM/s278gVsX7Ie3xhPUACpz9/KtDETSNviHNP+pl40Te0IQEh0OFtWbSKltTN1pHqjmuzavNNvBzYtWZsv7pqnb6F6w2TE46FF97ZsXrGRTUvW0qxbaw6lHSSuTrW/1Fd/bQUdX9xz9H3+VqaOmcB+N9/2JetJ8orb5yfXtmLEAIRGh9N37FA+/79XOJSeyR9L1lO7tTPdJbFRMnsKuLb+4rrddDFtUs8GICw6gsw0Z2bX5uUbSExM4JXn32HJouW0aetMy2nYuD6bN231mz8oKJDnX3+SMQ8/w5bN2/Laz2zfioXzl7Bk0XKqu9NyqjRMYr+ffu5Ysj5fzJk39aKx17+lLLePfy7fSHRiJX595Ru//THGGGNO1qncRUdEbhCReV6PG3y6EwFscZ+nAfmmAYhIMPAgzmB1ifnbF/h+iPvw1hJnMWwzVU3Fuci3qOpb7v75lXHunADWAY+LyGvAApwdbebi7GrzB4A7ZSYCZ+7+EaCyiFyLc3NwB/C2u23muUAPYImb41KcD/FNdyefGhyb5hMAXKaqHwChQF+gLbBDRP4tIoV9bTOxaZ+OdHtgAA17tWXnqs10vqvfcQG/T56Pd8yaqQtpcXkXEprWosPQ3gwcP4yGvdqx8P2pNEntyKAPH8AT4OHrsZ/QNvVs+g2/ijN6tmfJtAVUTalO7zv7H5f/t8m/5ov76tmPuebpW3jg63+zbsEqVs5awm+Tf6V9amdCYyL+cl992xr1agfAysnzadanI+c9MIBG7jm6+JzDN2b11IW0vLwLVZvWouPQ3gxy8+XGdX9gAI17teXPVZs5t4BcuTGrpvqfQtfxHxcTk1iJC0cO5urxwziwJ402qWfTZ/ggWvZsz7JpC0hISaTnnZcfd9ySyb/mi5v1/g+06dOJWyeMwBPgYeWMRQB0vfFiXnnhHbIys5j89VT6XNaLBx6+i16XnM/UyTOoW782d91//Mywywem0rR5I4b+awjjP38tb/ecs8/twNw585n89VQa9+nAOQ8MoEGvtuxatYWOd116XI7Vk+cfF7N26m8sen8qjVM7cMWHw/EEeNgwYwkAZ97Yk19f+YajWfnXXxhjjDF/F6r6sqq29nq87BOSwbFpN5H4r7vvBZ5T1X0l2TdRLdurgE+UiAzE2Saz0MUMbuwZOHP1v3XnunfCWTC7FnhbVb9w4yKAG3AW6l6MM7WmAvAZzgfzE/AVcKWq7hSRwTjbck4GjuZ+aCJSC+iuqi+6u/E8AtynqunuFJzz3bjXcHbweQjnm4WngMmq6r1w9zhPNR2itTo1ZdPclRzYud9vTGh0OEXF+NokhwmPjqBhp2asnruCtJ37Cow9kbhOHVuVeF+9p92FRodTu1NTNhZxjqJiAIKiw6njxmUUkquoGF975Chh0RE06NSUNXNXkF7IccWNA/g8fUXe8+iYKDp1ac/cOfPZ+efuYvXLnzuqtaNmp6ZsLuRahUSHFxnj61/zR510nwoTFFe7VPIaY4zJx//80tNk3xXnnLLCtsIH0wp97249WEVVnxSRkTjbpL/vEzODYyVMC+BjVb2ev6jcFfjlgYgEFjElJ5/RyQNK5YPcJKUzypqkRW0MdOJKa11NdtEhJ2WPnNBHXGzeBX5J+WdksxLPCVbgG2NMOWAFfgHcH0/9CeeHVy/A+b2kfgUNQovIdHdb9b+sPCyyLXdOtLg3xhhjjDGUqV10VDVNRLrg/LbSE6q6HWc79oLiu5TUua3AN8YYY4wxphSo6l6O7aRzyliBb4wxxhhjygUt478we6qUx110jDHGGGOM+Z9lBb4xxhhjjDHliE3RKSdKa6eXmL/RP5HSulstrWtbWUvn2l4U1bDEc6Zklc6qpaOzPy2VvJyVWippbXceY4wp48rQItvTyUbwjTHGGGOMKUf+PsOzxhhjjDHGFMIW2TpsBN8YY4wxxphyxEbwjTHGGGNM+WBz8AEbwTfGGGOMMaZc+VsU+CKSICIdCnm9hoh4RKSSiFRz4yNKoR8eEcn3rYeIVBaRkL+Yu5KIXOE+DxIR+Sv5jDHGGGP+12jOqXuUZSVa4LuF7vdugeoRkVgReU9EQkUkXESCi5knSkRGeDUNBs4oIDYa+AKoDzQB7gSaA4+ISJQbEywi0SLSXUQeFZEYtz+3iUhLn3wLReRjEVkpIt1EZIuIfCsi3wLfAv7233sNeLQ4783rPLeKyM1eTRnA4yLSDHgb+EFEpriPvSJS4UTy/68IjYmgVscmhMVGnu6umNNg/8Es5qzazN4DWSd1/K49exl8810l3CtjjDHm9CrpOfhPAJHAVmAR8AdQE9gJ/AQ8BfxQVBJVTXdH5a9X1VeBK4EsEcktrveqah8RiQfeB6bgzLpKBC4FYoEg4CHgLqA20B9IAOoA/wKmu22LRaQjsFVV1wHrVPVSEXkcSAe+V9WrC+qrO+q+EggTka6q+oPXa2cDHwCrgYaqGu91aDZw2I0LACoCdwDbVfUKn3NMBw4Vds0uemIIcSnVWDNtET+NnVjsmIi4aPq9cBtv9nsYgM7/6kty2wYARFauwK+fzqBScjxVUhJZOe03po77rMA+XDrmhnxxkXExDHr+dl64bCQAFWtU4eaPRxAaEcrW39by/oDH/Obq6dXXWWMnEhIVRp9xtyABHo4cyOLToWMJj42i70u3s+aHhXR7YCB/rtxEhRqV84452bw5R7Lp+cQQqjSoQVRCRZ46c6jfXBc/MYTKKdVYPW0RM9zz+baFRofT99l/EhwRys5VW5g07HUq1KjMhaOuIjQyjC2L1hEaHZ7Xp5kF9LuXV79nuv1OHXcLngAPhw9k8Ynb71xXjrmRhJRElk37je/GFbzXfEFxUXEx/OOt+xjT8148AR56zHuWA5v+BODw3gOEVolh+w+/8ft//fc3JC6atq/ezoxLRh3XHt2gOk1HDGRW/8ePax/x4Y+s+3MfnRrUYEi3VgX2d/SnM+nYoAadGyWzM+0gd771PZ0aJfHUlz/z8o09qRgZVuCxvvanpTPskafIzDq5mwNjjDFlUBkfWT9VSmwEX0TOAyKAx4BfgedwCuvHgUnASO/itxhuBbqIyPnATOBCoAcwFDjgxrTBKaBzgPOAvcDLwA5VHQSMd0e+VwNnA42BakBHIArYhHNTMMQ9HiBcRFoAVYBC91oSkW5AX+B54P+Au922XNnAZ6raBdgkIteKyNMi8gRwMXCpiAzEuSn6WFU/Ad7xGrmf4jUlqLC+pIrHwxupI4mMr0DFmvH5Ahr0aI1vTGh0OL2fuomg8GOzi378zye83X80b/cfzZ+//0HG7jQ8AR6e7/sQ0fGxxNVM8NuBJt3b5IsLi47g8qduJtgr/0UPDWbrsg082fh6KtdLpFqLOvly1Xf7+lbqSKLiKxBbM54ml3Tgl1e+5oOBj5Oxcz91Ojcnrm4i3496l1njPmfvpj8JrxR93DEnmzc3bvfabQSGBvu9ng17tMbj8fCam6tizXi/bc1TO7H4s1m80e9hgiNDqda0Fufd258Zz07krX4Pk9iiDlHxsbzpdYy/z87j8RwX0+SSDvz8yte85/Y7pXPzvPjm3c/EE+Dh6b4PEhMfS+UCPrPC4vrcP5CgUOcLt2oNkvlj4mx+Sn2Eta9+x9GMg/x40QjCEmKJqJU/d1BMBK2fvZnA8Pyz1pqOGIgn+PhxhR+WrCc7R3l7aG/+TDvIxp37/fZ3wbpt7E7PpHOjZADWbt/LXRe3Y0jXlpxVrzort+zye1xBAgI8PDnqPiIiwk/oOGOMMaasK7ECX1W/B+4B9uNMV2kMzMUpulcDTUQkwB2t9st93ePmO6CqA4GuwAjgBpwR91rAe27MJJxvCrJUdZyqfqOqo4GjIvIV8ArgUdVs4BlgNM4Unmdxbg4eB1bh3JB87XbjW/f1g0AacL5Xsb3aq5+3AzcBg3BuZroBlwP3icjjIhKLU+D3cUfgq6jq68B/VPVu4DNggqq+izM6f8Q9f6CqdlPVbu7zo8W4/F2Wf/UzABtmL6dGm/r5ApLbNcI3JidH+WToWA6lZ+aLr9asNmnb91C1QRKLJznHrZ29lJp+cgPUbtcoX1xOTg7vDX2GrIxj+SNjo9i1YRsRcdEcPnCIyvVr+O3rCp++zn9nCutnLgUgvFI0B3ansWHWMrYuXEONMxtQrVktFrw3tchrUJy8ye0asXvdVo4cPETG9j0k+clVs10jlrm51s9eTlKb+n7bDu5Lp1LtqoRGhxNTtRL7t+6mUq2qbFu6HoDgiFC2LlpXZL99Pzvvfke4/c5Vt10jFkyaA8Cq2Uup06ZBvpyFxdVr35jDmYdI37kPgFot61Ltgjac/flDNL7/crZMmgvAnzOXEdc2f381O4dfbnyWIxnH/7tKvqIzO2ctzxc/b+02zm/u/ELsmSnVWLhhe76YI9k5jPr4J6rFRjJt6QYA2tVLpFlyPPPXbWPpHztplpz/5qgwkRERREWW+FIdY4wxp5HNwXeU9CLbi3FG8EcAlwG93OfdcKa73A+ki8g+fw835rrcZCLyKvCQqu7CGVEPUdUvVfUbr3NuBzp4j3oD7d34Qaq6x40LwZmSFAiEqeqDQCXgUvfm4A/35mM/MANYD1QHJnsV3FvcXO3cY69U1UzgaiBWVffhfMtwCGdqkvcI/jYRCQO+dNcNFKSB1/toXkgcInKDiMz78MMPr/h53WIADqVnEhkXky82ODyE9O17j4s5nJGZr7jv+ei1DB4/jP6v30X1VnXpeE0P9u9wLmFWhv/cufl94w5lZJLlkz9jdxoVk+Jpc013ti3dQHjFqHy5gnz6GuF1zsRWKYTGhLN14Zq8tkYXtQMR0rft8XvMieYNjgylQY82TB0znpzsHL/vOSg8hDSf6+mvbdOvq6hUK4G2V3dn19qtZO4/wPJv5tL59lTqdm3pTin67S/1O7FVCufeczmDxg9j0PhhdLnmAvbv2Jv3WUQV+pkdHxcQFMAFt/Xl8zHv58VtXLyWn/o8zIzeI9GcHCKSqgBwND2TkLj8/5SPZmRy1OdzD46NpEbfjqx+4at88ZmHj1Alxim0I0KC2ePnhnPS/FXUjo/l6i7NWfrHTj5wb25Ule9+W0dggIcAj61LN8YYY6Dk5+DXAu5Q1Z9F5FIgTlVf9Il5uDiJRKQrgKpmuTvKnAUkAS/5hAYCGap6iTtNaJ2qrnUL5INurktw5rcH4kwj2i8iB3Hm6iflJlLVbLffv+EU+LuAHu4IPDjTeVDVWcAsEflURKp49fk2oCXQQFVXi0hb77ekqpki8hzOtwcFWeHeTOB1Xr9U9WWcKUnPpL+/7NYtG9cQHBGC+Cl0Dh/IItCdclFQDMBX979OSHQ4lz53K+8NepxzRxybqhESHkpBm/scPphVrLiEBjX44LZx7F+wjsvfuIuKtasWu6+hMRF0H3kVH9/0zHHx3z3wJtWa1aJmx8Zs/e3Er4Fv3kp1qvH7d/M4lHYQCfAUmCvIJ5e/tq53X8ak+1/nUEYm7a+/gJb9zmbG2Ikkta5Hhxt7sXPVlrycJ9PvHiOv4s3Ukez3mp7SccSAYn0Wh/x8ZufdfAkz3p5MZtrBvLitKzeSkubMEDu0K42IWs5IeWBEKOIp3hhB42H9WTZ6PHo0O99rYSFBHDrifFGVefgIOZp/NtrKLbvp27YBcdHh9GyVwrhv53FFxyaICPenduC5b+cxY/kmuvuZ8mWMMeZ/SBkfWT9VSnoEfyvO7jU/AR8Bt3mNrO/OnX5TFHG2uBwN3Oc23YuzmHamiIz0CQ/yej4CyJ0GlI17A6OqE4HZwEhgAvClqn4JXABUcRfr+loObAa+VdUu7qOud4Cqpqpqx9wHMAe4U1VXuyGBHJuik3tz8EoRaxFaeI/gi59tOf2Yn9SmHgDxDZPZtzn/XORtS9ZTVEyu+uedkTeqvHnJemq2dqZhVG2UzN7NO/0eU9w4QWh4bisCQoKoXL8GB9xpIN62L1lPDa++7t+8C09QAKnP38q0MRNIc4vZ9jf1omlqRwCy0jOpUD3uuGNONm9YTAQtLuvCwPHDqJgcT+1OTfLl8nc9/bUFhQZTpUENxCMktkght3bdvnwjMYmV+G3C9Hx9Km6/+z5/K1PHTDiuuAf4Y8l66rifRWKjZPYU8Fn4i2vQoSlnDz6f28Y/SGKjmlz5+I0MfnooMY2SwCOExEXjCXb+JxfTOIkDf/jP7SuufUOaPHAFnT4dTkzjZBrd0y/vtUaJcSxcvwOA37fuoZqfb3VqxEWzeU86AMs376JqbCRvTPuNL+etAiA96xBRYcXapMsYY4wp90q6wH8WZ8vITTgF+jM4i1CHActViz1j6UKcxbOBIvIWUE1Vn1XV/wKVROQzEantxq7D2V7yNWABzoLVuThbZ/4BICIX4ozc5851rywi1+LcHNwBvO1umxkOxONsyfkWzhz7YhGRC4Cqbh9zBXBsis4rvod4Pffk/q2qcblTglQ11p2DH0zh96QTm/bpyPkPDKBRr7bsXLWZc+7qd1zAysnz8Y5ZPXVhgcnqdG7GxrkrAVg2eR6tUjvRa/hAmvVsx8ppC6mSkkj3Oy877hh/cf589dj7dL6xF3ctfZXQ6HBWfPULnX36+rvb124PDKBhr7asmbqQFpd3IaFpLToM7c3A8cNo2KsdC9+fSpPUjgz68AH2b9lJteZ18o7ZuWrzSed9/eIHOXwwi+3LNqA5yrcj3+VcP9ezWZ+OdH9gAI17tWXV1IV+22Y+/wUXPXYd9y19lbAKESz9YjYAZ93Yi59f+YblX/1Csz4dOc/rs+tSwLnO8/rsWl7ehapNa9FxaG8GjR9Go17t8uIXT/6VNqlnkzp8EK16tmfptAUkpCTS687Lj8vrL+6/l4/gmf6jeKb/KLYs38D7977EN89+Qutx/6DrD4+xfcpvVDwjhaYjBpJ4UTvSf998XLFekO873MlPqY/wU+oj7F+2keVjPsp77ZwmNflqwWqe/GIO3y9eR534WMZ9++txx/dpU59f127l2ue/5MPZyxncuRl92zZk0oI1XPv8l2TnKO3rVS+yH/68Oe6JkzrOGGNM2WNz8B2ifr4OP6lEIik4i1bfBybiLIithLMg9mZgoqouO8GcvYBIVR3v034Fzgh7NM5NxVrgbVX9wn09AmdRblecdQHhQAWcha334mzZ+RXOHPqdIjIYOAqE4kyf+RyYhXNTMMfND07BPltVh/n0JwT4GejqNecf94YhSlV3+MT3A0YB16vqLHeazxeq2g4fIvIeUFdVzyzsWj3RdIjW7tSUjXNXcqCAXUhCo8MpKsZXhuQQFh1B3U5NWTd3BRmFHHcicc07NmNTEX2t1alpoTEnc0xx83rH7SskVx33eua+X39tBcldbV6cz+VEPrsdcpSw6AgadGrKmrkrSC/isyhO3LlZx9bGB8VEUKVzE3bNWcmhYn42BbngZWdLzLSDh5izejNn1KpKXPRf39Um8Cx/P1fx1wXF1S46yBhj/reUqQVQO8/rXDKFbTFU/v7HMvXevZVYgf+/TkSiVTWt6EhwF9vmqGqhe9u7sVGqml5U3KjkAaXyQWZI6dyixujf4keUgWPbG5W0AreT+ot2SHE2Xjox3gV+Scot8EuaFfjGGHPKlKki1wp8R0kvsv2fVdzi3o3Nv01IwbFFFvfGGGOMMabsT505Vf4+w6jGGGOMMcaYItkIvjHGGGOMKRdsBN9hI/jGGGOMMcaUIzaCb4wxxhhjygcts+teTykr8MuJA6W0202do6Wze8qmwJLv78FS+vm68FL6oiuD0lnof212Vonn/C4k/49PlYR3b5lRKnkryy+lknfcvDElntN25jHGGFPSrMA3xhhjjDHlgs3Bd9gcfGOMMcYYY8oRG8E3xhhjjDHlgubYHHywEXxjjDHGGGPKFRvBN8YYY4wx5YLNwXeUmxF8EakhIh4RqSQi1UQkQUQiSvF8Hp+/mxTjmAQR6VBafTLGGGOMMaZcFPgiEg18AdQHmgB3As2BR0QkSkSCRSRaRLqLyKMiEiMi4SJym4i0LCTvP0Xk0gJeniAi7d24RsCbIhLsc3yUiIzwahoMnFHAuUaJSBcRGS0i97rHficipbNPZTFEVa5AYsfGBEWEnq4uFCksJoI6HZsQHluy2ziGx0RQr2NTIko4799BQIVIojo1J+B/8L2XlF179jL45rtOdzeMMeZ/jqqcskdZ9refoiMi8cD7wBQgB0gELgVigSDgIeBVoD+QANQB/gVMd9sWi0hHYCvwJzAZyHSPrwwcFZG7gTQgDOiuqhnAf4BrgTk4NxQxwGSRvA/8KVX90v1m4XpVfRW4EsgSkVQ3Zq+q9hGRSDf/WUAVIB6oCRxQ1ezcbwtUC//i6dIxN1AlJZGV035j6rjPTiguMi6Ggc/fzouXjQQgoUESvUdeTVR4KBVqV2XRi18x/z/+c3Z+8noqpFRj09RFLHz2cxoN6kqdi9sBEBwdzp8L17J72UbqXNyOmDpVCY4MZcfqLbzQ+4EC+9hnzBAqpySyatpvTB830W9bVOUKXPnSv/j9hwVcOHwg4658mJDwUPqOuobQyHBCY8I5lJHFimkL+b6Q63H5mBuJT0nMi4uuXIFrXrqDZT8sYPC429i7cQcrvp+f14+T7W+uiLhorn7rXp7ueR8Vq1emz6hrSGxaC81RZr/zPT8U0td+Y25w+/obP4z7jNCoMAaOvRVPQACHDmbx7tBnINuJTfr3UEJTapA2bR7bn/0oXy5PVDi1nrsLCQgg52AW6//xbwJio6j98r2k/TCP6g9ey+rLH+DonjQufOJ6KqVUY+20Rcwe+7nfvvnGhESF0XvcUDwBHg4fOMTEoWPJOeJ0rvsjV7N22iJ+/eHH43L844lbqJ5SnQXT5vPx2A8LvA4xcRX4vxfuYXi/+wCIqxbHrU//C1Vl24ZtfHL/qwwaczNVUxJZOm0BX4/7tMBcvnGeAA+jZzzHzj92ADD+odepUjOBc6+5kICYqoCA5pCdtt1vvv1p6Qx75Ckys0r+9wiMMcaY4igPI/htgA9wivvzgL3Ay8AOVR0EjAd2AmcDjYFqQEcgCtiEc0MwBDhPVTNU9SzgJmA38DTwKLAfGKqqHVQ1Q0Q8qjpbVYeISHOgFlBPVbuoahdgHFDJ7d+tQBcROR+YCVwI9ACGAgfcmBg3/j6ggXvufwIpIjID2Ay0LuwiNO7eBgnw8Hzfh4iOj6VSzYRix4VFR3DZUzcTHB6SFxdfN5FFX85mz4o/+GP6YmJqVyW6Vny+fLUuaI14PHx+ySgi4mOJrhXP8nd+4Mt+o/my32i2z/2dFe9NZfk7P7D09e/YPH0xv0+Ywd7NuwrsY6PubfAEeHi574i8Pvprq1KvOl8//A7Tn/uc1TMWU71JLS6690omj/2U6a99RUh4KF/9ezzR8bHEFXCupm7eZ/s+mBeXUK86Ex9+mx1rtpC2Yy/fP/VRode0uP3NdcH9AwgKdb7s6Xnvlaz7dSW//7iI9b+upEazWgX2tYmbc1zfh4hx+9qqd0d+fPVrXh70KOk791G/cwsAKvRohwR4WNXnHoLiKxJSs2q+fBX7dObPVz5nzYCHOLJzL9FdWhFWL4nNI19j+9iPSPtxIWFN6zi5PB7eSR1FVHwssTXz/zuo16N1vpjGl3Rg7ivfMH7gGA7s3Eftzs0BqN6mPhGVY1jzw8LjcrTt0R6Px8P9qfdQMb4iVf30GSAiOoJbn7qd0PBj3yydP6AHLw1/gYeuGE5ctTi6XtcTT4CHJ/oOJya+IlUKuKYtu5+ZLy6xQTK/fjmTp/uP4On+I9j6+yZ++24uT/cfQfb+bejhA+RkpfnNBxAQ4OHJUfcRERFeYIwxxpjSoTmn7lGW/e0LfFWdBCwCslR1nKp+o6qjcUbevwJeARR4BhiNM9r+LM6NwePAKuBX4GsRqSgid+IU6FcB6Tg3BzcCb4jI1yLSGrhbRJaJyBA3tibwnoh8LyIP+/TvgKoOBLoCI4AbcL45qAW854ZluzmeArbgfNPQFhjmnvtDVZ1b2HWo064Riyf9DMDa2Uup1aZ+seNycnJ4f+gzZGVk5sUt+nIO8XWrk75pJyExEWz4bh5V/eSs2r4hayc5vxq6Zfay42LCE2IJi4th15INebGbZy0jLC6GJZPmkFxAH2u1a8gSN+fa2ctIblPfb9vaWUv5Y+Eaap7ZgOrN67BhwWoq167K5qXrSWnXiG0rNxEWFcaa2cuoXcC5Uto14rdJcwDy4lbNWsrGhWs445KOoMofC1bnnfOv9BegdvtGHMk8RPrOfQDE1U6gQtWKLJr0Mxm70vhz7bZCP7tF7me3xv3sZr/7PatnLgEgsmI0Gbv3O8/bN2Xvl7MASJ+1hIgzG+XLt+vtb0j/aREAgRVjOLJrH+kzF/0/e+cdHkXV/fHP3U3dTbIkJCQkpEEQEnonQCgiRQGBUEQEFQXbK+Jr9wWRIv4AxQLYsWHDCgg2pEvv3dBbKCGBkN5zf3/MZNlsJhRNNOL9PM8+bM5+58yZM5Nw5s65d8nefgCvNjFYmt5A1tYEvGIbkfCDdizH1u2llkF84W2jy2i2fbKUY2v2AGCp7kP2+TRMLmZumXovaYkp1O3WvJSPhm0bsu6HNQDsXreL6FZlYwYoLi5mxsPTyc7Itts+f+lTTh1KBMC7mjfBN4SydfE6APav20NUq/qGvm5o26CMrnazujTt3ponv57MPa89gsns+GdSIFw9kfnZhv4AvKxWvL0qbfqPQqFQKBRX5B/foqNzFmgvhFjqZLcBw6WUF4QQ7tibF/CUUo4XQtwEdJdSPgWgj8abAC9grpOvfLRWn9NSyqlCiJLn8z2BL9HaddoCnZ2DE0LMQXsCkCuEqIHWmrPIQeICTASGoxX5BWhPDlro+z1icMzvnDhxovPZs2eDAdqP6Mnmr1YAkJuZU+5os5vFnfSkC6V0eQ6FvSMe3hb8agaQezGT/IwcfMLLjty6WtzJOpMKQEFGDjbHkeqPHgch6PP1WACCY6NJ+GIl+z5ZSp67ieoGI8HOMeZl5lA9ItDQVkKj3m3xCw9k1AdP4eFt4X8rX8MvJIALp5I5sHYPkS3q4V/uvjxI0/22HtSZwKhgWg3oBGjF/7HthyguKi6zzz8Sr9nVTJcx8aSeTKZmTDgPznsOD28L7e/sQfLhM9Tr1JhFUz4lsG6tcveT5nTuSghvXhdPm5UT2w8BYLK4U3D2PABFmdmGI/glWJvXw2yzkr39gN3m2ycOWVAIRcWYLO5knNXOcX5GDr7hZa8t18toQppH4WGzcnr7YRrf1omUQ6fY8PZiBn/0JO3HDuG8HmfD2EYs+/JXALIzsgkKN445x+B6feDFhwiuHYKtug2vat7UbXoDKz76SddnE1Du74MHF/WclugS1u7m5dueJz35IrdPupeGXZqza+kWAIS7F8V5meXmUqFQKBSKqsD1UuC7AJlSyn5CiG7AESnlYb3gzxZC9AMe03VWIE0IkY3WZx9W4kRKuRPYqRf+K5z20VlKWaaRV0qZpffdLwF8gAWOnwshuuq6XKEJ2+n7fMdBFg68ANRFmxzcFG2Ev2R/Pxkc8/1hYWGEhWnhr/nwJ1nS9uFm8cBhLkAp8rJzuRodQHZaJsc/WkqDu7rh3yDcUFuQlYeLpysArlYHf0JQlFfAgr4T7dp2E4cT0j6GVU/OwatXC5wWIrKTn53nFKPJ0FbCovEfkXMxixMHTrBj8XoiW9Zj6CsPceC33eRn5+Fmcb/Mvi7lY8OXywmsE8LSNxcA0P/5u3C3elC/azOKCor+dLwdH7yVjXN/Zc+PG/ENDeCtIdrDnuFvjKFRrzZs+fY3TGZzueckv5xz52mz0m/C3cx98FW7tjgrF6FrzRYPMBn7NFfzotbk+zh639RS9pPj3qHmE0Ox3dSK4qxcXDwczrGBr/ysPEONh81Kt4l38t0DrwMQ1CCcHZ+vICs5je/HvEnjJ/sz/f7/A+Ce50fi5qG1iXlYPTGZrv4B49v/e5PA0EAef+Mpno1/ihGP32nPlYfFA9NV/D6U6E4lHKcwvxCAs4dPlWrvMbl7ldt7r1AoFIq/H/VFVxr/+BYdHVeH9xMAs776TBHgIqVcAKxDGyX/Elikj6DfDNTQJ+o64gHc5PRyoyyeesuOCegOPArYryx9mc4paL31AM+gTQheI4SwV75SyrXAV8Ay4FPgMyllAbAN6IvWQnRZTu0+SkRLrXUiOCac1MTkP6Xr98I95GflEtSqHu4+Fqw1q5NhoE3edZQgvWWjekyYXVOzTT3ObT9cSluQnUtemjbtoGZM2GVjDNdjLNEZ2eIe6EPT+DgAPHws5KRrvk/tO4bJZOLCKc1/SEw4F8rZ18ndR4hsWb+U7sYHbqVlfBwndx8hILImOenZFRJvnfYNaXNnN+6dN46aMeEMmjoKgAO/7cI/LJDVc3647DlJ3H2USKdzZ3Y1M/yNMfw4fR6pp1Ls2uzdh/HS23I8YyLJTzxXxp9wdSHyzac4PXUu+XquAh+Mx29AFwDMPlYK07PI3n3Y3pYTGB1GmkF8Z3cfLaMxuZrp9+ZoVk77ivRT2ih96rEkqoXV0PLSKJJkh7iO7D5MdKtoACKiIziXmGSYByOsPlb+O+sJZj85k+yMbE7sPkyUfl5rxUSQUk5OjXQjXhlNrehwhMlE0x5tSPz9OADVawXoTZfyquNSKBQKheLv4Hop8I8AU4UQ76MVxV7AJrSlM08KIW5BG7nPQ2t/CRBC3IN2Y/AYMFcIYZ8RJ6XsJKW8yel1o9M+PYBxaCvt7AUW6z8f51Jeb0GbAOwihPgYCJZSzpRSvgZUF0LMF0LU1rVvoxX444CGQohItEnB+UDpZmUD9i7ZQrP4OHqPG0bjXm1JWLGdGlEhdH988BV1Rqx6exG1YxvQ8rF43H29COkQw4X9p2j1ZOlVQ4/9spUbBrQndvwd1O7dhhPLdgAQ2qkxZzYmlNKaXV3xCq5O7Pg7aNirLftXbCcgKoSbHh9USvf7ki00je/AzeOG2XVGts2fL6dZ/w6M/PI5TGYT+1fvAuDG+/vw6+z5NOvTjr7jhtO0Vyz7VmwjMCqEm53ysXvJFlrGx5XSrf98GS37xxE79CYC64RQt2PjCol3zm2TeX/IC7w/5AXO7DvO18+8B4BfeCDFRcX0fGIwTXq15fcV2wmMCqGnU6x7lmyhRXwcfcYNs+ta39aFWo0iuenhfjw47zma9NZWL7r4ywb84jsTMv4efHu3J3f/CWo+eUcpf9WH3ISlUR2CRg+i7lcv4NunAymf/4JffGfqfvMiwmwiY9V2Lv6ygYb929P1uTuo37sNKQdO0fGJ0tfBgSVbS2kOL99Bk9s6E9QoknYP92XovLFE927Dzi9XERYbzR1fjaP5nTex8N1LKwZtXLKBTv27cPdz99C+dwe2Lt9Crbqh3P5E6biNiH9oIAEhAYyceB+T5k0h40IGbeI7MmjcXbToFcvuFduoGVWLvo8PKbXdjiWby+h+mPkNI14ZzXM/vsSRbQdIWKvNcYiJa4IsuPqVcT6aPf2qtQqFQqGoGKT8615VGSGreoRXQAgRhzZp9jAwV0r5vW63ok1o7Yq2PKUPMB9tFP034AdgqJQyWQhxJ1CINlL+HmDUZCvQbhIek1Ju09e3Pyil/KyUSIj+wP+h9f5v1m29AS8p5Twn7e3APrTVfN5Cu1F5EYgBpgNPAEnAN3qspYfEHXg64nbp6WOlblwjjmz6nczktHJzdrU6gPpWb2rFNeLMxgRyytG62SxX1Dhrt2zdd9l9e/hYiYpryLFNCXadkc2RbEpPaff0sVIvrhGHN/1OxhXyUZ7O08dKo7jG5e7zz8Sb5zAS7Olj5Qb9nFwp1ivphhZpE0DNNivecU3J3LiXQn1S7x9lVbVAIuIacXJTAlnl7NfDx3JFjTObKb0ajdXHSpO4puzbtJeLfyLmAOGOxcdKdFxjDm76nfTL+LpaHcDsLdP+cEzl4epf+8oihUKhqLpUqZ6YEy27/mWFbdiWZVXq2B35xxf4VQ19Mq+UUuZf43YuUspC/b0ATFLKopKf5RVO1NMRt1fKiaxdWDnfs3XCpeLXl3Iu8CsKSyU96MqrpFaPkgK/IvnFVDlfeuVc4FcUAcL9yqI/gCrwFQqFogxVqsg93vymv6ywDd+2tEoduyPXyyTbKoOUMu8Pblfo8F5yacUfrlTcKxQKhUKhUCgUJagCX6FQKBQKhUJxXaBW0dG4XibZKhQKhUKhUCgUCtQIvkKhUCgUCoXiOkE1NWuoAv86wVRJc1wKKulJl4eseMdmUTkTgl0q6Y+Fn6ycB2h1+xZUuE+XxVe3Ks61chLPSvF7n7z65Syvha8aj69wn4N3Tapwn6Am7yoUCsW/GVXgKxQKhUKhUCiuC1QPvobqwVcoFAqFQqFQKK4j1Ai+QqFQKBQKheK6QFZCC/A/ETWCr1AoFAqFQqFQXEeoEXyFQqFQKBQKxXWBrJwvtf/HoUbwFQqFQqFQKBSK64h/XYEvhAgVQpiEENWFEMFCiCAhhPUv2neQEKL9X7EvhULxxzHbvPDq0BSzr/ffHYpCoVAoFNfMv6pFRwjhA3wPDAVqALcCS4CeQojxQB7gAcQCnYBpQAEwClgtpdzu5K8PkCKlXK///DkwQkqZp//sDTwupZygb3InkAusNYhtErAc6AZkAG8A3wC3SCmLrnBo7z/w7QT2r9jBitkLyhXFTxtFjaiQUjpnm4ePlSGv/wc3qwfnDibibXbBNyqY0xsTqNmqHt8NmFzG740vjcQ3KpjjK3ayZeZCu73TlLs5vmInx5ZeSpunvw9Dl08j5cgZDq3YyZpZxvH2nj4K/6jgUhqrvw8D3xrDx4O0GIIaRtD12dtx9XQj4afN+NUNISAqmIMrdrKqnDz0nTaqjMbZ5uFjYeDr/8Hd6sG5g6dwcTHbY/mtnHj7OMT7m0O8g94aw0d6vN6Bvty7cBIXjp0FIOdsKrbQAI6s2MnGWQsN/XafPpLqUcFlNBZ/HwbMfYpPbhmHu83CLa8/hJuXJ+cPJMKGGQC4Dx2DOSiUwr1byP9lnqF/AOFdDc+HJpM9bfRlbSXUmjYa96hQMlZs4dzsr8p8bvK2EDbrKYTZRHF2Liceno4sKATAxb8akR9P4GCvR+36YdMeICgqhD0rtvPz7O/KjdNZZzKbmLR6NiknkwD46vkPOL3/JK4ebtzw7VQO3PIoodNH4xFVi/QVW0maZRxrxOwnEWYzRVk5HH/4Jcy+3kS+8yzpyzYT8tw9HLp9HEUX0ktt12bGSGx1gzm9bCd7Xjc+dx7+PsS9N4Zf+1/6nfGJCqbp2NtYPeLVco/zcqRcSOWxsVOY+9bLf2h7hUKhuN4pVpNsgX/RCL4QIhCYDywFioEQYCBwG+APPA/UBh4D+gOtgP8CrYEhQDUhRAchhOO3x6wHntL9twdSS4p7ACllBhAqhBipm4YCQ4UQK/XXfH1bLyAdaId24xEFRABZUsoi/YlDeecqHjC/PWACPoG+VI8IMhQ16NEKk9mEo87I1jy+A9vnr+HdwZOoERWCp78Pi+9+mRv6tcPdVvZBR+2eLRFmE9/2n4Q10BdbRCAANVvXwxJgK1XcA/R4czQms5mP4ifiHVgNP13vSP2eLTGZTKU0Hj4Wbp3xAK4Wd7uu58S7WPTEO3wUP5Fmt3fBw9vCnAHl+43u0RKT2VRKY2RrEh/HzvlreX/wZAKigrFW9+HD+Il4XSZeYTKV0nj4WOjrFG9Iszr8NnsBc4dMYdNHv1BUWMQX8ZPwCvSlmoHfKD23RppO44bi4uEGQEx8B36fv5YvB07GzeqBKawuLk3aIUwmsl95AmHzQwQEl/Ffgnv/keDqdkUbgE+PWDCbOTzgKVwCq+MWUbOMxrdvZ1LmLODo8PEUJqfi3am5/bOa/7sH4XEpJ017tEaYTbw84DmqBfoSUM71a6QLqR/OlkVreW3IRF4bMpHT+08iTIKRb/wXFx8rtp6xCJOJg/FP4xroZxxrv06ce28hh4eNpzD5It6dmuNRN4xTk94nafbXZKzejqVhnVLbhN6sne8lt07CM9AX78iy587NZiH29QdwcTj/XuE1aP7c7bh5WwyP8UqkpWcw9oUZ5ORWzpd4KRQKheL64V9T4KMV7F+gFffdgFTgXSBJSjkcmAckAx2BBkAw0AHwBk6g3RCM0rdFCFEL2AP4CiFWAlOABkKIi0IIV4f9PgJ0FkJ0B9YAtwA9gYeBLF1jA6oDzwL1gfPAf4AoIcRqIBFoWc5xdQa+Aji8bi8RreoZimq3jWb34o046oxs2amZBNSpiYePBb/wQA4sWE9xkWTDS19jMpe9XEJiozm0SPORuHYvwa3rYXIxc+O0e8lITCGy+6XiLqRdDJ6+XmScSgHg2Lp9hBrEG942hn0/bCilKS6WfPfwLPIycuw6z2pW0s9cAMDs6sLRDfsAOLJuH+EGfiPbxrBn8YZSGiNbTmoG/g452L1w3R+K91uneGs1q0vL4d24Z/4E4h7pzwE99yfW7SXEwG9obLShJrRdDAXZeWQla98um5OaiV+dmrj7WPAOro68cA5z3UYUbPsNgKIDOzHXaVDGP4D5hibI/FxkRuplbSV4tW1E2mLNb9a6nVhbxZTRnP/0RzLX7NB8+dkoPK/FaY1tTHFOLoXJl/zWbduAbYvXA7B/3R7qtKpvGKeRLrJZXZp0b8VjX0/i7tdG26/Pz599l/zEc3i1bUjqD2sAyFi3Cy+jWD/5yR6rS3UfCs+nkbl2J9nb92Nt3QBLkxvI2pZQapsa7aI5oV/zZ9fuJaB12XNXXCRZ88AsChzOf0FmLqtHvW54fFeD2Wzi5UnPYrX+sRsEhUKh+DcgpfjLXlWZf02BL6VcDOwEcqWUs6WUP0kppwCFQogfgPcACbyOVqw/DsxEuzGYChwANgM/6i7zgZ+llJ0dX8AOoNBhv1lSymFAV2ACcB/aE4FI4DNdVoQ2Yj8DOAUEAW2AscD9wFdSyk269h1gJbDyxIkT+4HRsbGxL+3IOEReZg5e/j6Gx+9qcSctSSuGS3RGtmNb9lM9Ioh2d/cgPyuXi0fOUJCZQ3ZyGibXsh1drhZ3Ms9qBVt+Zg6e/jbqDejAhYOn2PbWYgKb1qHx3d0wuZpp/Wh/kvcepyhfS09eRg5Wf5uhzwzdZ4kmPzOnVLEMcHLLAVre1Y2Gfdvh4WPh+Ob9Dsdi7Dc9KbWUxsh2fMsBqkcE0fbuHuRl5XL+yBl7LEZ+3Zzi9Son3kMrd/Jh/AQ+6D8Bi683Lp7aCHl+Rg7WAIN4PR1yq2tMrmZix/Tnt6lf2nWnNu+nWkQQzUZ058Lh08jsTHDzQKadB0DmZmPyrlbGP2YX3G6+nbyFH17e5oDJ4k5Bkua3KDMHF39fQx2ApXk9zDYvsrfvR7i6EDhmCMLNFc+YSGrPe5Ha816ky4ibuahfg7mZOfgY5BfA3eJeRnd812FevW0CrwwaT056Ng26NEMWS9LOpeqxelBwVou1OCMbF3+DHBjEWkK1Ph2QhYVQVHpJBheLO9n6eSnIzMHD4NwVZuaUKu4B8s6nU5xfWEZ7tXhZrXh7/SXThRQKhULxD+df1YMPnAXaCyGWOtltwHAp5QUhhDtawQ3gKaUcL4S4CegupXzKYRsT0FIfvXckCDDjUOQLIeYAD0spc4UQNdBaeRY5bOMCTASGoxX5BcCLQAu0G4kjDtr7S96EhYUBvL5+/fovno0Yut7N4kF5nTz52Xm46i0dJTojW48nb2PB2A/Iy8whqn0jonq1Jnn3MVwtHmBws1qQlYeLh/bAwtXigTAJAhqGs/fzFWQnp7H/u7W0fWoQ7jYruz7+leBW9RAmzZGb1d3+vlSsWbn29pPyNAA/PPs+EbExdHp8IKd3HnE4FneEMPCbnVtGY2S76cnBLNJzULt9Q2Juac3ZPcf+dLwntx6w39xkX0jHV29HcbV6gEG8BdkOudU1rR/qw465v5KXnm3XxT01mKX/+5D8zBxajLyZG2K7QV6uvcVGuHsa+nfrNoiC1YshJ+uyNkeKs3Mx6S02Jotx3KBNUg2ecD/HH/w/AAIeHMj5uT+Q9uNa3EIDOTLkfwCcf/5Be/7dL3P95jmcpxLdqYTjFOr5TDp8ihpOLTjFWTmXYrV6gsnYt9nmRa2J93H0gaml7Keee4egx+/Ap2srLi5eY7cXZuVhdrzmy8mBQqFQKP56ZLH6mwz/ohF8HRcgU0p5E9oE2vv192lAthCiH1oP/gTgJeC/+kTaYCDMyVceWtvMAOBzoJ8+gt8DrQ0IACFEVwC9uBdoffbdnHyFA28Bd6HNBVgM9EUr8GOBjZc5pq1orUTUjAkjNTHZUHRq91EiWmqtBCU6I5urpxtB9UIRJoHZzYxPuNZf7B8TRlFeQRm/53YfJVhvUfCPCSMjMZm0Y0n4hNUAoEbjSDJOpVArriGN7+pGaMdG+EUF03vaSAKjw0lLTCnj8+zuo4S2ugGgXA2ALJb20fU9C9YS3lLbJigmnFSDbU7vPlpGY2Rz9XQj0J4DF3shHhgdzkUDv2d2HyXMIV4jDcAdc5/Gq0Y1XDzcsFb3sc9pqBETRrrBeUvafdTellOiCe/QkKZ3dmPwl2MJiAmj+7SRuHi641+/FsIkqNmsDkhJ0cmD9rYcU0gkxRfOlfFvrt8M14698RwzFXNIbW1SroHNkezdh7C01FpdPGMiKUgs61e4uhD2xtOcnT6XglPacXm1b0L1O3tRe96LeMZEUmuqNnn3xO4jRLXU2nJCYsI5b+CvPN1dr4wmJDocYRI06dGaxN+PO8V6GGuraC3W6AjyE5MMY4148ylOT/vEHmuNB+Lxje+i5cjHSlF66ZudC7uOUkO/5qs1CCOrnN85hUKhUCj+Lv5tI/iOvfETgBFCCDPaiL2LlHKBEKIjsAKtuC6QUi4SQnwB1BBCBEopS6qEeWg3AV3QVr95Dbgbrb++FTBSX35zCtBH3+YZtJsBkxBiopTyeQAp5VohxFdAW+AnYK+UskAIsQ2t0J90mWNaAPzWa9wwbujchDf7j6dGVAhN+rbj1xlf20X7lmzh/q/H4xPoa9chKWM7fzyJgS/dT7UQfxJ3HsYvKpgO4+8grHNjCrLzaPPkQDa+9I3d75FftjLg23FYA30J69yYb/pOQBZLur48irq3tsXsYuanB2aSpbc0uHp5cs+WWeRl5hDTuw3fPTybzk8MYuXLl2JNWLKVu79+Du9AX6I6N+GDfs+Xe/CdnxjEsv+bR9LvJ7jrG22bup2b8NXo2XR9fBDLZpT2e+/XlzTv9n8eJGVsF46fpf9L92ML8efUzsP41wmm+3N3UKdzE757eDZdnhjEinLirXOZeFe/Pp87vxhLUUEhGz/4mSb9O2ByMRPZuTE/PPwG7Z8YyNqXL+X20C9bue0bLbeRnRvzeb8JJCxcb/988JdjWfL0HIKa1KbHjPvwCfHnzLaDFKxYBSaB5dGXMNmqY45pQe6H03DrfSf5i+fat8957dIDKc8xU8n7vHR/uJEtfckG6nw9DddAP7w7t+DE6OkEPj6MpBmf2jW+t3XDs1EUNR4eTI2HB3P+0x85ctuz9s9rz3uRxGdmAbBzyWYe+3oitkBfGnRuyvT+YwmKCqFV3w4smnGpDclIdyrhBPe8/ggIwe6lW9i/dnepWNOWbKDu11NxDfTDp3MLjj38EkFP3MHZlz+za/z0WIMeHgQPDyLl0584//kvRLz5NNWHdCf3wHEyVpeeKH7y5610nz8Oz0Bfgm9szJoH36DJUwPZOf0bFAqFQvH3IuXfHUHVQMh/USaEEC5oo+6jgGzgQ7Te+w/0Vxe0gvpbtCI9ANiFNjH2BbRR//5ANeBttMK7j5TyeX0JTj+0XvsItBV76qCN/n+F1sefLqUcrccyG23i7uNSyiP66P5wtFV5jqNNwn0JCAX+I6XccplD8/38P69fOLopgUx94qURHj5W6sY1xFFnZHOktpcXoXGNOL0xgexyfLvbLFfUOOv9Ojbk+KYE+0TRsrFaqB3X6LIaZ8w2C3X0bcrLg4dPWY2RzREv7yvH8kfi9fOxEh7XiMQr5PZKGmfuu1XrVcfTC5f6zSg6tMdwwuy1cHSx2f7e7GPFK64ZWZv2UJh88U/5fRtPPH2sRMc15tCmfaRf5hivVgdwn8y1x+od15TMTXv/dKwAewu0OS5uNgtBHRtxbkMCuVd5Xspj8K7L3b//cVz9a19ZpFAoFH+eKtUT83vdW/6ywjb64I9V6tgd+dcU+EKIOLRJs4eBuVLK73W7FW3ia1e0ZSx90IrzZ4DfgB+AoVLKZCHEnWi99avQ2mpO6ZqSHvlgYLKU8hOnffcGvKSU85zstwP70FbpeUv38yIQA0wHngCS0NbDHyqlPFze8T0bMbRSTmRIUeV0caWaKj7cgkr6NXOppF8RayXNwLcX+BWIY4FfkbyNZ6X4LSnwK5qSAr8iUQW+QqH4h1Olitx9dXr9ZYVtzOEfqtSxO/KvadGRUv4GNDOwZwGv6i/Q1qNv5SDp5qCd62A/pf/r3JtvtO/F5di/KHkvhBgmpSzU328GupZ8wZUQoq38t9yJKRQKhUKhUCj+FP+aAr+qU1Lc6+8ll1byQRX3CoVCoVAoFFdGfZOtxr9tFR2FQqFQKBQKheK6Ro3gKxQKhUKhUCiuC6r6N8z+VagRfIVCoVAoFAqF4jpCjeBfJxRTOW36RepG+NK3lv1DOLWk4n0m5lgr3ilQ5Fl0ZdEf4GKue6X4zXa9sqaqUJBy5MqiP4BanUehUFRl1KxFDTWCr1AoFAqFQqFQXEeoAl+hUCgUCoVCobiOUC06CoVCoVAoFIrrArVMpoYawVcoFAqFQqFQKK4j1Ai+QqFQKBQKheK6QC2TqXFdjOALIR4QQng6/PyoEKKugS5UCGESQlQXQgQLIYKEEJWzPIhxnEFCiPZ/1f4UCoVCoVAoFP8+qnSBL4S4UQjxmxBiqRBitf5vyfsmQgg3IYQr2nE0E0JY9E2HAcecfPkA3wP1gIbA40AT4AUhhLeucRNC+AghegghXhRC2IQQFiHEGCFEM4P4+gghYh1+/lwI4e7ws7cQYoLDJncCLco51klCiM5CiClCiGf0bX8RQpivNW8KhUKhUCgU/0ak/OteVZkq3aIjpVwOLAcQQqyTUnZ0/FwIcSfQG22p8o6AmxDiBSAM+FUIIYDFwFzgc2Cprg0BBgK+gCvwPPAEUBsYAgQBdYD/Ait12y4hRAfgtJSyZIHp9cB7QH99ZD5VSpnnEH+G/tRgpJRyDjAUyBVCxOuSVCllfyGEF5AOtANqAIFABJAlpSwSQph0f5ddkn3gtPuoERVCwoodLJ89/5p0Xv42hr35KG8PnghAteDqDH7lIQIjayJMJnZ+tITNsxYa+rtp+kh8o4I5tmInm2ctRJhNjFjzCmknkgFYOf5jzu9PBMDs7sqo7W9ybv9JDq3YyZpZCwx99p4+Cv+oYLvG3duT+NmjMZlN5Gfl8u3DsyguKMLq78N9P/8fqSfOcWDFTlbNNvbXd9ooAqKCOeigMbIB9J58N34RQbhZ3Dm4Yie/zVqA1d+HYXOf5p1bxtp1t06/tP1q/TicbdVCA7hl0l24e3lyaucRlrzwGS2HdaVh77b41wnG3cuDlIOn+bzPeMO4u08fSfWoYI6s2MlGh/xb/H0YMPcpPrllHE2GdaVen7YAuPtYcN29l6TxswmaMga3OqFkrdrC+bfmlfFt8rIQ/NozCLOZ4uwcTj06FQoKAQh8/iGyVm8hc8WmMts1evV+vOqGcG7Zdg6/anyduQXYaD7nv2zoO6GUvcXcJzg4/RvS9xyz24ZPe5CaUSHsWbGNH2d/Z+jPSGcym5iy+g2STyYBMO/5Dzi9/wQAof+5leLcfLwaRmKpG8KFZds4/qqxb9cAGw3mPM6Ovto58AivQb0ZD2DydOfC0m0cf/VbAOq9+iDRN9Ti5PKdbJ9p/LvQ8eWRVIsKtmvcbBa6zHoIN6snqQcSWfPshwiziSHrXiFd//3A7ApFBQA893+vcuTYSTrGtuL+u28v4z/x9FmmvPImWVnZNIqpx5OjR1FYWETPwSOoFRwEwP/++yA31IksN4/OpFxI5bGxU5j71stXvY1CoVAoqjZVegTfiUJng5RyLlqRPRfYDLwGPAj0QivalwKvAK2AL9CK+25AKvAukCSlHA7ME0JUAw6i3Sg0AIKBDoA3cALtpmCUvj1CiFrAHsBXCLESmAI0EEJc1J8qlPAI0FkI0R1YA9wC9AQeBrJ0jQ2oDjwL1AfOA/8BooQQq4FEoOXlktOgRyuE2cSbA57HJ9CX6hFBV63z9LEyeMaDuFkufTlQm6Fd2fPTJk6s3k3ynmP41w+lWkRgGX91erZEmE18HT8Jr0BfqkUE4h8dxv7v1/PtbVP49rYp9uIeoMfrD+Lq6cZH8RPxDqyGn4HP+j1bYjKZSmka9mvPhvd+5LNhU8lMTiOqUxM8fCzc/vHTmFzMl/UX3aMlJrOJOQMuaYxsAOGt6lGzYSQZSamlPus+9g5c3d0u+dRjfN9hv0a2bs8MYfXMBXw4aDI+QX5EtI1my6fL2PjRLxxbtYs9X64mPTHZMLdRem6/cMhtCZ3GDcXFQ4tn56fL+Oq2KXx12xRObdrPxS9/xqt7O4TZxIkhT+BSww/X8OAy/n1u7cKFD+ZzcsRYCpNT8YrTHi55tmyAS4CvYXEfeEsrhMnE+t7j8Qj0xRJZ9jpzsVlpMvNBzJbSXzYVPKA92cfPlSrum/VojclsYvqAcdgC/ahRznVrpAupH87mRWt4ZcgEXhkywV7cB4QH4d+jFXlJFxEmE9t7j8Mt0A/PcmKNnvlwqVhD7rmZo1Pnsb3XWPy6NMW1ug/+t7RGmEx8328SlkBffCLLnq+Im1uW0dQd0IFD361l0YDJuHp54N84Er/oMA4vXM8Pg6bww6Ap9uL+15VrKS4q5rN3XuFcynmOnzxVZh+vvvUBD9x9O3Pfepmz51LYtG0XBw4f5ZabOvHR7Ol8NHv6NRX3aekZjH1hBjm5uVe9jUKhUFRliqX4y15VmSpb4OvtMSuEED8LIX4GGgohluttKz8LIdYIIToBA4BlwKfAC4AH0BhtFPyYlLJISrkY2AnkSilnSyl/klJOAQqFED+gjcKbpJRFwOtoxfrjwEy0m4OpwAG0m4gf9RDzgZ+llJ0dX8AOHG5GpJRZUsphQFdgAnAf2hOBSOAzXVaENmI/AziF9gShDTAWuB/4SkpZttpyoE7bGHYt3gDA4XV7iGxV76p1xcXFfP7w6+Rm5th1v7z8Ff4RQRxcvBEPXy9OrNlDsIHPWrHRHFy8EYCT6/YS3KoeNZtFUadHSwZ9+xw9Xn8QYdYuM986NanRMIILB7TC5di6fYQa+AxvG8O+HzaU0mz9ZClH1+wBwFrdh6zz6RQXS07vPEzG2VQAjqzbR7iBv8i2MezRj7lEY2QzuZi5depIXNxcSDt13v5Zi9u7UJCdR2byRbvPiLYx7NVjPLpuH2Gt6hnaqkfW5MyeowBknU/D3dti3/7Emr1YAmzsX7SBEIO4Q2OjOaDn9sS6vXZNaLsYCrLzyEpOK6X3CvTFEmAjb+8hLK0bkf7TbwBkb9iJpUWDMv4vfv4D2eu2A2D2s1F4/iK4mAl64REKTp3Dq2vbMttUbx/Dme/XA3B+zV782hhcZ0XFbL/vdQozLl1PrtWsRE8YTsHFLPzax9jtN7RtwNbF6wDYv24PUa3ql/VXjq52s7o07d6aJ7+ezD2vPYJJv86GvXgf2UdOEzysK+cWabFeXLMHW5uyvmVRMXvve5Uih1gLUjOw3FAL1wAbws2FwrQsqrVvwLnvtf2fXreXIIPzVTM2miP6+SrR5KZmYqtTEzcfC9aa1ck8dZ4azaOI6NmSPt89R5dZD9q337x9Fz1ujAOgTYsmbNu1t8w+jp04RUy9KACq+9rIzMpi194Elq1ez/AHH+fpCdMoLLz6bwc2m028POlZrFbLlcUKhUKh+MdQZQt8KWWalLKLlLIncA/aaPw+4H4pZU8pZQe0tpZfgebANLTe+/vRiulGaEV5CWeB9g59/EuBWLSWmOFSygu6zh2tdckF8JRSjkcbXR+o3xyc1HUmoKUQYqXjC604L9U3L4SYAzwvpUzR9+cupVwkpfxJl7gAE9HahWYArwJn0Pr1wwCj75x/58SJE/s3bdqUsWnTpoz2I3qSnqQdQm5mDl7+NsO8ulncy+jyMnPIdShwHLV+9Wpx4cApMhJTsASU9enq6U6mXmDnZ+RgCbCRtPMI3wyewtcDJpOXnk3EjU0BiBs3lKSdRyjK1+5/8jJysBrE6WpxtxftzpqQ5lF42Cyc2n6I/MwcTC5mCvO1EdC8co7b1eJOelJqKY2RrWl8B5IPniL50Gm8athoc1d3CnLziLm5NUunzSvr0yFGu08n276fNtHp0Xhu6NqMqE5NOLp2j3370LbR7PxkKfkZOVivIrfWABsmVzOxY/rz29Qvy+ib3tWNnZ8sBcDk6UFhknaTUpSZjdm/Whl9CR5N62O2eZG7cz+2fl3JP3SC8+99g0fjG/Ad3qeU1mzxIPeMdv0UZuTgZhB3YWZOqeIeIOL+XpxZtAFLRCDN3nqEuJXTafPdeG4ccQsX9esxJzMb73LidLN4lNEd23WYl297npcGPUdOehYNuzSnfruGuHm6c2zal5itHtTo206PNRu3gLK+izJzKMrILmW7sHwH1dpGU2vkLVxcswdZWITZ4kGeftz5GTl4Gp0viztZZ1JLaZI27ccWGUSDe7pz8fBp8tKySN55hEUDp7AofjJ5adkIN624zsnNpUaAPwBWi4XzFy6W2Uf3Lh1484PPWLlmA2s2bqVti6Y0iK7LR7On88lbM/D29uK39ZsNc2iEl9WKt9dfts6AQqFQVDpSir/sVZWp0j34DvRHK/AbOxqllNuFEIeAeUA1KeUJsE+o7YRWNJfgAmRKKfsJIboBR6SUh/VCP1vfrh/wmK61AmlCiGy0Xv0wp5jy0NpmrGhPEb6SUl4UQoSjtQKh++yqx5qrzwlop/t6x8FXONrTh7poE3+boo3wlzQN/0RZ7g8LCyMsTAtrzYc/SVe9ZcPN4oG2q7LkZedyNToAYTLRYFBHvuw3gdD2DQy1+dl5uHhoHUmuVs1fSsIJexGfevg01SICqT+gA4kbEvCq6WdvNXGzuiNMBj6zcu3tJ44aD5uVnhPv4psHXi+lNZlM+vG4lxOj4zFrGiNbzQYRbPliOXU7N+Hk1oM07N2WsBZ1SdxxmNz00kVgfpbD9nqMRrbVsxYQ1vIG2t3fm53f/kZ+tjZFoyA7j9DYGJY8PYeoni3BIO4Cp9wiBK0f6sOOub+S5xQPQhAaG82a6V/R2AOKs3MRekuRyeppmGcAk82LwOce5NToKVqOY+pw8cufKUpJJf37FQT8905SP1lk1xdm5WL21Pyare4I09WNEdgaRfD7hE/JOnSagBub4t+pEb8//wnuU++058zD4oHpKq7bEt2phOMU6tfZ2cOnqBERhBCC9d+uJOTMBTL3HKNax0Z6rB5QTg6ciXhiEHvunAZA1JQR+HZqTJHDcZdc584UZOXh4ln6d6HVM4NZ88yHFGTm0GjUzdS7rSMHvv6NYj3utMOnEebmSMDi6UlennZ9ZOfkUlxcdsrN/Xffzrade/jw82/pe/NNWCye1KsTiZubFltkWC2OJ5Zt7VEoFArFv4sqO4JfghAiGHgArYh3tNfSV5hxRSuys/VJtwApQLKU0rFv37EvfgJg1rcvQr/RkVIuANah3Rh8CSySUi4CbgZqCCEcG2/nofXoPwYcRuv/B62//l09Ritau8+z+mfPoE32XSOEsN98SCnXAl9xqdXoMyllAbAN6IvWGnRZTu0+SkRLrW0gOCac1MTkP6Xz9LES2bo+R5fvID8jh4CYMNINtOd2H7W37pRour/2IP7RYQiToE6PlqT8foKITo2p3a054XGN8IsK5rYPniAwOpy0xJQyPs/uPkpoqxsA7BqTq5kBbz7C8mlfknYqpZS2pO0lKCacVAN/p3cfJbzlDaU0RrYLx5PwDa3B6d1Hqd+tBRdPpRDa/AaCYsK4e95YgmLCuXXaSADO7D5KmEOMFxNTDG0AZ/cdxxZSnfXv/WiPKS8rl7w0bQpGjXJym7T7qL0tp0QT3qEhTe/sxuAvxxIQE0Z3PZ5aretxZsdh+7a5ew/a23I86kdSkHiujH9cXQh5/VmSZ3xE4Wnt8/zjp3EN1XrVPRrWpeBU6e3Sdx7Bt7XW6uLTIJyck8bXjzNZR89iCa8BgK1JbXL03JzYfZiolpq/WjERpJRzPRrpRrwymlrR4QiTiaY92pD4+3HOHT+Lf6j2a1qYloUs0NpVvBpEkHuVsboH+uIe4o/J3RXvRrVBQsbOw9j04/aLCSPDIM6UXUcJ1M9XicbF0x2/+rUQJkFAszpIKeny+oP46b8fET1bIgvzAYipF2Vvy9l/6AghNcv2+QPUr1uHM0nnuHNIfwCenfwyCQePUFRUxLLV66kXVfuqjlOhUCiuR1QPvkaVLvCFEDXRVsGZKKVMRxsZD9A/ngj8D61F52mgH3BOCLEQ2A8cEUJ8IIQI1fVHgKlCiPfRCmcvYBPa0pkn9f3dgnazkAcUAAFCiHvQbg4eA+bqy2YG65qLQKGUchnwiBAiAq3//owQojnahNovABchxMdAsJRyppTyNaC6EGK+EKLkf+O30Qr8cWjzDSLRJvvmo7UgXZa9S7bQLD6O3uOG0bhXWxJWbKdGVAjdHx98RZ0RnR+6FVeLG42GdeWeDa8TPbAj5w+cIvaJgaV0R37ZSv349sQ9dwd1e7Xh6PIdbHptPj1ee4ChP7/ImW2HOLlmLz8/8ibfDJzMl32fp7hYcuHoGWJ6tyH5QCKdnxhUymfCkq007t+Bbs/dQUzvNhxcvp1mt3WmZqNIOjzcl+HzxhLTu61d6xVgo9tzd9CwVxvOHUyk6+Nl/TWJ70DPcZrmwIrthratX64kMjaGlkO7Ur9bC9w83SnIzeOb0W9wYssBzu47zvdPzykVY4/n7qBB7zYcWL7d0AbQ7v7erH/vJwpy8+0xubi64B1cnU7P3cENvdpw/sAp2jvl9tAvW4mOb2/XHFm+gy8HvWCfUJu87wRL9HgiOjUmcWOCfdvMX9fj0+9Gajw7Cu+b48g7dBz/R+8s5b/awO54NIjC/8HbCPtkKt63dCTtmyVY2jQm7LPp+A7txYUPSq88k/TTFkIGxRE9cTg1b40lI+EkNzxT+hoz4sjsRYTf04O2iybiF1ufk5+vAGDHks20ie/IoHF30aJXLLtXbKNmVC36Pj6k1PZGuh9mfsOIV0bz3I8vcWTbARLW7mbX0q1YbFaaLpiIX/cWmFxdqDPxLgJujSUr4SSRzwwxCq8UR1/6iqbzJ9Bu3/vknk4hdc0eUn7aTOCgjrQdfwe1e7chdf8pWj5Z+nwd+2UrdQe0t2tOLNvBjtnfEzftXu76/T3cq1k5vGA9216bT+eZDxC/5EWSth5CFmjtTF07xrLol+VMn/kuvyz/jTqR4cx89+My8X3w+TfcOSQeTw8PAB4YMZRnJ7/EwLsfpknD+sS2KrOir0KhUCj+ZQhZRRfyFEKEoRW8z0gpv9VtN6KtjmMGLgC3AYFSymNCCBfgJWCelHKjrr8H2Ar4oE2YPQzMlVJ+r39uRZv02hW4FbAA1YD5aKPtvwE/AEOllMn6E4JCYBVaW80pXVPSIx8MTJZSfuJ0LL0BLyml81OI29HmFZwA3tL9vAjEANPRlu5MAr7RYzhMOTwdcbv09LFSN64RRzb9TqbTBExHrlYHEOntTVhcI05tTCC7HK27zXJFjbM+IK4hxzcllJkoWoKHj4XacY0uq3HWHt6cUO7xePhYqKP7K9EY2Yz02ecqxqcz/j5WwuMakXiF3F5J40wvD61X3OTjhbV9M7I376EoJfWqti2PI2k+9vcuNiv+nRpxYf3v5F9lTOWx0LMIi4+V6LjGHNz0O+kOE5mduVodwO05l2L17dSYtPW/k3+Fba6Ei81K3o0tOLMxgZxyjtvNZqFWXKPLapy5e8ck+/u09AzWb95Oy6YN8a/u96firSxc/dUTAoVCUYoqNZS9ITj+Lyts257+rkoduyNVtsAHrZdeH7m/7hFCuJS0FOm9+iWr+iCEEPIKJ+rpiNsr5UQGF1fO92yli4oPt6CSfs1MlfQr4l1Jj/dKCvyKxLHAr0gWel79ii/Xwu1l54xXCAdd3a8sukYcC/x/AqrAVygUTlSpIlcV+BpVepLtv6W4B3CcL6AX80VOPysUCoVCoVAoLkNV643XW8OjgR+llC8YfG5Dm9fpAmQCt0kp851110qV7sFXKBQKhUKhUCj+iQgh4gGzlLIdECyEqGsguwN4RUrZDW1J954Vse8qPYKvUCgUCoVCoVBcLX/l+vRCiPvQ5nKW8K6U8l2HnzujrZIIsBxt9cWDjj6klG86/BgAGCx7d+2oAl+hUCgUCoVCobhG9GL+3ctIrGgLsoD25axR5QmFELGAr5RyQ0XEpgp8hUKhUCgUCoWi4skEPPX3XpTTGi+E8ANmoX1xaoWgCvzrBPdKmk6RVgmr3QDkV4JfUyVN5K+cdYQgxVT2m0orgoPptgr3WcOcV+E+ARoWWSrFb4aonNV5jrhU/Dk72GZ0hfsEOJBV8dcBQK89ZeaIVQhqdR6FQlERVM7/rH+YrWhtORuAJmjf01QKIYQbWhvPs1LK4xW1YzXJVqFQKBQKhUKhqHgWAMOFEK8Ag4G9QgjnUZJ7gRbAWCHESiHEbRWxYzWCr1AoFAqFQqG4LpBVaFl+KWW6EKIz0A2YLqU8C+x00ryF9mWnFYoq8BUKhUKhUCgUikpASpnKpZV0/jJUga9QKBQKhUKhuC4oVl8NCqgefIVCoVAoFAqF4rriLy/whRCeV1b9Yd/+V/j8Acf9CyEeNfpWMSFEqBDCJISoLoQIFkIECSGslRGz0347CiFC9PduQgihvzcJISprMReFQqFQKBSK64JixF/2qspcVYEvhKgvhHDV3w8SQtyuv/cVQvQw0L8ghLilHHefCiG6CCHGCSGWCSGWCiFW6//+JoSoqfu4VwjhKYQIF0LUE0KMEkK8IYSIEkLUK8f3j0KIMIN43PT4TUAzIUTJ2nzDgGNOWh/ge6Ae0BB4HG1poxeEEN4O/nyEED2EEC8KIWxCCIsQYowQopnB/vvoX2BQ8vPnQgh3g/h7on3rGcAkYKkQIhn4GbipnGNWKBSKKkfKhVTufPCJvzsMhUKh+FdytT34HYFpQojBgA3s63Z+CLyt//wC0AqQaN/U1VMI8QjgDsyUUs7XfY0D3pFSdkQrmgcAXaSUDzvt8yzat4N9CwQDdYFQoAsg0NcSFUKsBTL0/dQD3tUHvgHcgPcAV6A32vKoHQE3Pd4w4Fd9pHwxMBf4HFiqa0OAgYCv7uN54AmgNjAECALqAP8FVuq2XUKIDsBpKeURPY71ehz9hRDtgVQppX1hcX1JpIn6MbcTQrym+w4HnpJSPlDOeXkfiAZ+LOdz+k4bRUBUMAdX7GTV7AXl2gCs/j7c+fHTvNVrLK2GdaVh77b4166Ju5cnyQdP8f6t4w330Wf6KPyjgjm0Yie/zVpg9zXorTF8NGgyAN6Bvvxn5QyKi4rISc3krX7PkX0ho4yv/tNGERAVwoEVO1ipx2Zks/r7cPubjzJn8CQAajaIYPj7T+Lu5cHxLQf49O7pfzofvSffzcGVO4np3tJ+fGtmLTD029shB2sccjDwrTF8rOegWmgAvaeNxNXTHbOrC/n5BexfsYMVs419AsRPG0WNqJBSOiMbQN/JI9i/cif8sBWAZm8+TGC3Zhx+YzEHX5tv4B3c/G20nPMo6/pNBMAjyJcOP04m61gSAFtHvUb++dLnKXLGQ3jWrcXFZds4/fo3ZXyavS1EvfUYwsVMUVYuhx6YgSwoxMXfRt33nuT3/uPs2htfGolfVDDHVuxky8yFhjGWp+k05W6Or9jJsaXbcfP2pOcbDyPMJgqy8/h91GvIgiKavHIfXnVDOLdsx5/KQfTY2/GqG4L7qj9/vjx8rAx5/T+4WT04dzARxr+KyceLkFefwLNBFBJI/WQRKW98Wca/yctCrZlPg9lMcXYOiY9Mg+Ji6q58n/wTZwE4O/Ft8g5cWkq52Suj8KobQtKyHRx4zTh2d38fWs15lDX9JpWye9evRaMJw1g3ZGq5x2xEWnoGY1+YQU5u7jVtp1AoFH+WqrSKzt/JVY3g61/F+xMQ6WC+HfhCSvmzrhknpewhpewJfAqMk1L2lFJ2cSjukVL+DnQFe7vOFLSRant7jT6KvlxKORyt0O4NtAPqA/2AZg7+2uv7PAA8qO+z5HWjlPILKeVctCJ7LrAZeA14EOiFVrQvBV5Bu0H5Qt9nNyAV7SYjSY9lnhCiGnAQ7UahAdrNRwfAGziBdlMwSt8eIUQtYA/gK4RYqR9vAyHExZKnIsBp4FXguJSyMzAUaA2MALyFEEMNTks82ncwtQOC/SICywiie7TEZDYxZ8BEvAOr4RcRaGgrocf/7sDFww2AzZ8uY8OHv3B41W52fLWKtMQUjPZRv2dLhMnEh/ET8dL9efhY6DvjAVwtlx5StL6nB0kJJ5jecBSHVu3E06dsx1NMj1YIs4l3B0zAO9CX6hFBhjYPHysDZjyIm4P/Qa//hxNbDzCl4UhqxoQT0Tb6T+UjvFU9vAKqYXIxYzKZ+Ci+bL4cc+Cs8fCxcKtTDlrd3Z2VM75h/TuL8a7pxyejZuCjH5MRDXq0wmQ28faACXadkQ0golU9vAJsJCzbBkCtgXFUj40m+2gSHkG+WCPL7sPVZqXZzAdxcYixWvMoDr6+gPXxk1kfP7lMce97cxuEycS+W/+HW6Af7pE1y/itHt+RM+8uImHIRArOpWLr0gyzzUqd1x/BbPEo5ctkNvFN/0lYA32xGeS2Ts+Whprg1vWwBtg4tnQ7APX6t2f7ez+x8I5pZCdfpEaXpgTdol07a/s8/6dy4Nemvt1PRZyv5vEd2D5/De8OnoS71ROPRlFU69+FvCOJZK7eSva6HbhH18YtIrjMPmx9u3D+/fmcuGschcmpeHVsgUf9SNIWreb4Hc9y/I5nSxX3NfUc/NZnwmVz0NwpByU0mjAMk9u1r8VgNpt4edKzWK2V80VmCoVCobg8Vyzw9VaUVLSR7Nloo9RDgOHAKL1QtQkhyvWlt7SYhBBD9SJ3hD5q/iaQo8vmCSHi9PcRwHwhRFe0Qno7sA3Yor/PEkI0cvDfH+gP3KO3+iwVQmwWQryuf14P7et/l6HdfLwAeACNgUDgmJSySEq5GG190lwp5Wwp5U9SyilAoRDiB7RReJOUsgh4Ha1YfxyYiXZzMBXtRmMzl0bV84GfpZSdHV/ADqBQ12xFu2EKF0IcB6qjFfkZwK/AIwZp7cylZZeWh7cq27UU2TaGPYs3AHBk3T7CW9UztAFExsZQkJNHZvLFUtsfXbsHq7+NvYs3EGqwj/C2Mez7QfN3bN0+QlvVo7hY8u3Ds8jLyLHrardviFdANe6ZPwFbTX+M441mz+KNemx79XjL2mRxMV8+PJO8zEv+vfxtbP96FQDpZy8Q3rr+H86HycXMrVNHcjExmeaDO5c5vqvNwXdOOchJzcQ/KpioLk3JTcsmJz2bw+v2EmHgE6B222h268deojOymVzM9J86iouJKUR3awGAb6sb2PvcxxRk5pCyZg9+bcruQxYVs/X+1ylwyKNvi7pE3N2N9osn0mDi8DLb+LRryPlF67Q8r92Nd+uyN1LnPv6Z9NXaMr+u1W0UpqRBUTGHHphBUUZ2KV8HF2nHkrh2L8Gty8YYEhtdRmNyMXPjtHtJT0whsntzAHbPXcrJ3/YA4OnnQ15KGtXbxXD6e+28/JkcOPqpiPOVnZpJQJ2aePhYsNX0o+B0MoWpGViax5CxYjMuNQPIXLUFzxYxZfaR+tkPZK3dAYCLn42i8xfxbFof7+6xRHw5nZBXngDzpT/F/u2iOfX9Rj0He6leTg423z+TQoccAITd3onktfsMj/VKeFmteHtV+rQlhUKhKEPxX/iqylzNCH4BsFNKeRNaQT5Hf72s2/agFeE/CSF+FkJsRBsV/07/+We00f/aUsrPgQlorTRzgQTgY30/dwCvCSHqSCl3oY3ar9Y/b4/WilIyWn5MSrkbQAjRF62wPoY2Ml/y+ppLBbQFrVBuDkxD672/H+1JQiO0oryEs0B7hxuFpUAsUAMYLqW8oOvc0VqcXABPKeV4tMJ8oH5zcNIhxy31byezv9BacEomzrYFPgAy9eP4GijSc9sSSDQ4L9Zhw4a1FUJsiY+PfzHNu7CMwNXiTnpSKgB5mTl4+dsMbWZXM53H9OfXafPKbB/RJpqtny4jL0PTOuNmcSfjrO5P1+Rn5pQqbAEyU9KY/8hsPug/Aau/N0HRZaZK4GZxJz3pgkNsPoa2PAP/WRfSCWtVj0a3tsNSzYvigj+ej6bxHUg+eIo17yzGp6YfYfrNQl5GDlaDHLg65cDqlINbXryH4fPGUq9HS7o8OZhmQ7qQuPUAxYVF9mMywtXiTprTsRvZmsfHce5gIqveWURokzpE3NsDk6uZrCNay0ZhRg7uBnEXZuZQ6JTHc8t3sKb386zt/TzWOjXxdjpPJos7BWe1/RdlZuMaUNZvCV4tbsBss5K57QBFmTmlivsSX1l63vIzc7AYxOhioKk/oAMXDp5i21uLCWxah8Z3d7Prg5pH4W6zcnHbIVws7uSeuXDNOfCqG0xBWjbF+YXUGhxH7ZE97X4q4nwd27Kf6hFBtLu7B8mHz1CUlknOlr24+PpgbdOI/COJFJ67gIu/b7m59WxWH7PNi5wd+8nZdYDjtz/Nsdueoig9C6/OrUrlL0ePvSAjB3eD2I1y4OrrReiADhx664dyY1AoFApF1eVqCnzHFUXHoxWde4DJDvafpJQ90Iryc2iTVDcBX+utMl2llIcc9AXAx1LKafadSJmENmp9Xv+5EHgAbaQ9Ca2In4o2Mh4KIIRoBdwHDEK7mcp1eOWXxC6l3I7WgjMOqCulPCGlzAZ8gE5oI+gluACZ+s3LNOB+/X0akK3vtx/wGNrNykvAf4UQfdBuQJwr1zy0In0AWn9/P30EvweXbgCPoT0B2A/MB+ahFf9+JfkwIPPTTz/9UUrZ8rvvvnumlkfZBYTys3Nx1Vtu3CzuCCEMbXEP3sqmub+Sm166ACvIziOkWRTH1u/DzeqOMJXta8vPyrW39ZSnAUg9cQ6pX0mZyWlY/LzLaPKy8y75sngghMnQZsShVbvIPJdKm7u68fvSbTjMw7jmfNRsEMGWL5aTmZxGUsIJQppFXfb4rpSDH//3AZ8MmUJmUiqvtvwPGz/4GVeLO3XjGl32mPKz8xxi03RGtuAGEWzW490+fw3+7WIozMrFpOvMVg8o57w4k7r5AEVZWt905qHTeNUu3dJR7ODXdJnYzdW8CH9hJEcee6PcfRVn5WL2cL10LAYxFmTlldEENAxnz+cryE5OY/93a6nVThvpdq9mpePkO1n2xLsAfzgHW0e+xrp+E1kfP5mTX64mael2u5+KOF89nryNBWM/YPnM+SQfPk21gd2o8cRdZK7ZRtqC5eQdPol317bl/i6ZbF4EPf8Ap59+DYC8/UcpTNZvMI8klmrtKczKw6zv38XqgTBd3cJpDcYOYd+UecjCoqvSKxQKRVVBIv6yV1XmWpfJDEBr05nNpdFnAIQQXsA3+ms7WuF7qxDiCaclHl0ApJRL9Z9dHWz7pZQXdX/dgGpoRboF8NdfNi4V7pullL2AdLTJrs84vO5wit0VsALZQog7dVsKkKzfTDjqSpgAmPX4ixziXACsQ5sY+yWwSEq5CLgZqCGEcGwmnof21OEx4DDa0wXQVsx5V3/vhjYx+QJa7/0C4CjQFDgCGD0n36r7BWiSmphSRnB691HCW94AQFBMOKmJKYa22u0b0vrOboyYN5agmHD6Th0JaAVxTloWAIHR4Vw02MeZ3UcJa3XDZTUAke0aENWlCS4ebtRqFsWZPcfKibeeHlsYqYnJhjYjTu0+ah9dz8vI5s/k48LxJHxDawBQkJNvL4oCo8NJM/B7dvdRQh1yYKQB8Ar0xSe4OkkJJwhuUhspJTWvcEwR+rGX6Ixs54+fxU+Pt1bj2uQkppC266i9JcWnQTg5J4334Uybec/iXqMaZk83Ajo3Jj3hZKnPs3YdwUtvy7E0iCAv8VwZH8LVhbrvPM7J//uM/FPl7zdr1xF7W45/TBjpBnlI3n20jObisSRsYdrx1mgcSfqpFEyuZm5+czTrp35FxintnriicnB+/e92PxVxvlw93QiqF4owCUKb1QEpEZ7uFKWmY2nVEM+m9XANqk5+YlLZnbi6UGvWs5x76SMKTmtxBL/8BO71I8Fkwqd7LHm/H7XLL+46am/LsTUII/sqc+AfG02D526nw3fjsDUIJ/rpQVe1nUKhUCiqBtdU4EspQxx6yFsIIXzRivA2wFrgMyllSctNETAYbdLp70KIDnqh/DzwmBDiaSGEDa1/3b40hhDCSwjhAjyN1ucO2jKVI/VXr3LC2yilvKnkhbbajdR9tkRr0XkabZLuOSHEQrQR8yNCiA+EEKG6nyPAVCHE+2h9/15oTyO+B07q/m5Bu1nIQ3saESCEuAft5uAxYK6+bGawrrkIFEoplwGPCCEi0NqHzgghmkspE9CK/1vQ2ojy9Zw00P03F2W/P2AB2jyIV4DB5w4m0vXx0v8JJyzZSpP4DvQcdwcNe7XhwIrthrYPbpvMh0Om8OGQKZzdd5yFz8wBwOzqgi24Ot2fu4OY3m1IPpBIlyfK7qNR/w52zcHl2w1PztIXP6f9A30Ys+51pJQcXruHm5zi/X3JFprGd+DmccNo2Kst+1dsN7QZ8fuSLbQZ3o30Mxdo2KsNfyYfW79cSWRsDPd8+Rw1G0Rg8fOim0MOOhvkoHH/DnZNeTlY9eq33PnlOHqMH46Hj5V6XZrSqFdbElZsp0ZUCN2c4t23ZAvN4jvQa9wwu87ItvnLldSObcB9Xz5H2+HdOPzWYs7+tIVaA+OwRgQS3KctGfsTqff0YMO4HDkw41vafTuODj9M5vjcpWQdPlPq89SfN+I/oBNhz99N9T7tydl/klpP3V5KE3B7V6yN6hDyyACiv5mE363tDfeV+vNG6se3p8P4O4jq3YYL+0/R9smBpTSHf9laSnNs2Q72zVtFSGw08d+Mo9GdN7H9nR+JGdKZgEaRtBzdl/5fjSW4b1t7DmImDPtTOTj+8VK7n4o4Xyvf/J7+/zeS53e/j6fNi7RFq0h562u8e7anxpN349m0Pm6RIeQdPEHAY6XnQfgO6o5nwyj8H7qN8M/+D59ecaTM/oKQGY9Te/EssrcnkLVuh11/5qcthA7sQMMJwwjp05b0/YlXVawvbf84a+JfYE38C6TtPc7v076+4jZGfDTbeDUrhUKhqCxUD76GkFJeXqCNzC/Wi3rnz/ajtevcAQRKKY/r9peBlfqk1ZKVZM4At6JN0H0erbWmE1qh645WSNcAZqAV03FSypn6kpO9pZTP6L5mApuklJ86xBEFvCal7K3/3BqtHeYJKeUCvTgOlFIe028eXgLmSSk36vp70EbEfdBuOA4Dc6WU3+ufW/V4u+rHYEG7sZmP9rTgN+AHYKiUMll/QlAIrEJb6vKUrilZNjMYmCyl/MThGObovnqjjeI3BoZIKROFEC8Cn+grEDnii7Zaz+rxEXecwQAPHwt14hpxfFMCmclp5drKw8vbQm1dm1WO1sPnyhpnXWrKxXI0VqLiGnKsVLxlbUZYfKxXPK4/kg/vCsyBoz6kY0OOXuGYPHys1I0rrTOyOdIuV/uddrVZ8e/UiAvrfyfvKmK6HDVM9lVdMdus2Do2IWPDPgocJmX/EXb6+RMa14jTGxPILidGd5vlihpnIgq01pKKykGJn1+2V/z5Gm6+ZDP5eOHVoSlZm/ZSlJL6h+MFOJBls8ce0Kkh59cn/OnrAKDXnhf+tA8jXP1rV4pfhUJR6VSpXpWfA4dcvrCtQHomzatSx+7IFQv8y24shItTe8vVbOPuuAa8g90MSCllVb8p+ksQQrhJKfOvVj8+4o5KuaBdKunXJF9UvGNTJf2Nca2kHGRXQg7gUoFfkTgW+BXJJnPlLKNYUuBXNOs8Kv4acyzwK5KSAr+iUQW+QqFwokoVuarA17j2BY4duNbiXt/GsFLQl55U6FxLca9QKBQKhUKhqPqtM38V1zrJVqFQKBQKhUKhUFRh/tQIvkKhUCgUCoVCUVWo6stX/lWoEXyFQqFQKBQKheI6Qo3gKxQKhUKhUCiuC4rVAD6gCnzFFaiM1W4qi2IqJ9aiSnrcZ76y5A9x3LXiPacWV85qN3mV9If4rEtlZbfip2/ty65W4T4BzrlWTnJzJzxcKX6ZMLvCXaqVeRQKxb8VVeArFAqFQqFQKK4LilUPPqB68BUKhUKhUCgUiusKNYKvUCgUCoVCobgu+Oc0FlcuagRfoVAoFAqFQqG4jlAj+AqFQqFQKBSK6wL1TbYa190IvtBwvcznsUIILyFEz3I+DxVCmIQQ1YUQwUKIICGEtfIitu+3oxAiRH/vJoQQ+nuTEKKylgRRKBQKhUKhUFxnVNkCXwixQQixVgix0um1XgjRSNeECCEW6O+X60VxFPC+g5+GQoj79Pcm4CPAHbhJCNHPaZ8+wPdAPaAh8DjQBHhBCOGta9yEED5CiB5CiBeFEDYhhEUIMUYI0czgOPoIIWIdfv5cCOFucMg9gc76+0nAUiFEMvAzcNM1JU+hUPyr8KhRjRpxDXCxevzdoVQ6KRdSufPBJ/7uMBQKRRWlWIi/7FWVqbItOlLKtkZ2IcRHgLs+ql0I5Okj9tlSSimEKKD0E5rfgclCiItAAeAPfIZW5DcWQjwOPAycBT4HlurbhwADAV/AFXgeeAKoDQwBgoA6wH+BlbptlxCiA3BaSnlE3/964D2gvxCiPZAqpcxzOJ7bgIn6/tsJIV7TfYcDT0kpH7hMmt4HooEfyxP0nTaKgKhgDq7YyarZC8q1AVj9fbjz46d5q9dY3L09GTxrNIH1Q3Fxd2P9Rz+z4vXvDPfRf9ooAqJCOLBiByt1f842d29Pbps1mqD6YVf0d7U+S2z1ujanMK+ALV8st9ut/j7c9fEzvNnrf9cco8lspnpkEBnnLnJg+XbWzFpIrxfu5uCKnRxYtt0e463TL+Vx9awFhjYPHwsDZv6Hmg0jANj08a927R+JsTxbib9Hfn2J9MNnyE3NxM3LEwA3HwtJ2w+z8tkPALjxpZH4RgVzfMVOtsxcaN++05S7Ob5iJ8eWXjpGT38fbv3kKX7qMa7MOYp9eSS2usGcWr6T3a8vLPM5gIe/D53eHcMv8ZMNPy+hx/SR+EUFc3TFTjbMMvZVnsbi78OAuU/xyS2XYuz48kiqRQVzcvlOts809uescbNZ6DLrIdysnqQeSGTNsx/acxC/5EUyTpxj58odrHDIuTPx00ZRIyqE/Ssu6YxsAH0nj2D/yp3w46V8t37jIYJ7tCBh9iISXjPej7u/D7FzxrCyn5ZTW3QoTV+8m3O/7aXRc7ezvNfzyIIiu76Lw/neWk4unDUNhnclqo/2J9hdv35W6dePPY6hYzAHhVK4dwv5v8wrNyfCuxqeD00me9roy9quhrT0DMa+MIOc3Nxr2k6hUCj+bVTlEXxbOSPdJdwMfArEAc8abN9bCNFTSlkEDAeS0EbGF0kpe+o/3yeljJNS7gRaAV+gFffdgFTgXSBJSjkcmCeEqAYcBDoCDYBgoAPgDZxAuykYpW+PEKIWsAfwFUKsBKYADYQQFx3aiE4DrwLHpZSdgaFAa2AE4C2EGFrO8cejfVdSOyDYLyKwjCC6R0tMZhNzBkzEO7AafhGBhrYSevzvDlw83ABo3Lc9iTsOcWjNbnb/sIFaTaOoHhFUZh8xPVohzCbeHTAB70BfqkcEGdqa9G1P4o7DV/R3LT5jerTCN7QGB1buYP/y7QREhdh93vy/O3DVj+VaYlw750c2fbqUwrwCVr+5EO9AXxr2icUroFqp4j66Z0tMJhPvxzvk1sDWJD6OlMNnOLRyF0fW7iUwOsye8z8So5GthCGzH8FsNvNt/0lknrnA8qfmMH/wFM5s2s/ez5cDULtnS4TZxLf9J2EN9MWmx1KzdT0sAbZSxT1A+3FD7deEI2E3a35+7jsJS6Av3pFlrz83m4X2rz2Ai+Vyv8ZQV4/pi/hJeAX6Us3gWr6cppNTjHV7tkSYTHzfT4vNxyC2iJvLauoO6MCh79ayaMBkXL088G8ciZvNQs9Pn8Lk6sL3/Sbh45RzRxr0aIXJbOLtARPsOiMbQESrengF2EhYts2+ffAtLfFrHkX2mQt4BlbDyyBuV5uFVjMfwOyQU+8bQtjy6Dv8/sp3ZB1PxhpWw/5Zyfn+zul8O2Kk2fvJMhYOnsLCwVM4vWk/+/TrpwSXJu0QJhPZrzyBsPkhAoINcwLg3n8kuLpd0XY1mM0mXp70LFZr5XzxmkKh+Ocj/8JXVabKFvjAS5TfmiKklIuB24HVUspJgKcQYjXQFmgBjAQ2AUgpM9FG7w8C3YQQPwOdgLlCiGW6ZjGwE8iVUs6WUv4kpZwCFAohfkAbhTfpNwyvoxXrjwMz0W4OpgIHgM1cGlHPB36WUnZ2fAE70J4+AGzVjyNcCHEcqI5W5GcAvwKPlJODzhMnTkwXQmwZMWJEDxr5lhFEto1hz+INABxZt4/wVvUMbQCRsTEU5OSRmXwRgM2fLsXT5sWexRux+nlzfHOCXVt6H9HsWbxR97dX30dZ26ZPl+Jps17R37X4jGwbTV5WLnsWb+TIur3kpGcR3qoetWNjyHc4lmuJ8fCaPUS2jSbrQjqZ59M5uvF3ejx3BxcTk6nXrYU9xoi2Mez9Qcvj0XX7CGtVz9CWfTGD0BZ1ObBiO7aa1Tm0cidhreoR2e6PxWhkA6gdG4NnNS8unk4BIHHtXoJb18Ma5Iunv43k3ccACImN5tCijaU0JhczN067l4zEFCK7N7cfY0i7GAqz88hOTitzjgJjozmu+zmzdi81DM6lLJKsfnAWBRk5hue5hNDYaPbrx3Ri3V5qGfgqTxPaLoYCpxhDY6M5omtPr9tLkIG/mgaa3NRMbHVq4uZjwVqzOpmnziOLJOe2Hybr7AUADq/bS0Q5123tttHs1n2W6IxsJhcz/aeO4mJiCtEO11TYgA5kHD5D3rmLnFuzj+qtjXO64f5ZFDrkNHHhBrITUwjq2hQ3m4XMo2ftnwU7ne+aBj4vp7EG+WJxuH5KMNdtRMG23wAoOrATc50Ghjkx39AEmZ+LzEi9rO1q8bJa8faq9ClRCoVC8Y+nKhf4+Wi976V68NEK4JL/zd2BGCHE/UAOWg/7JiAFGCilvAAghIhGa3n5H/CrPoK/CrhTStnVYZ9ngfZCiKUlLyAWqAEML/Gn79dFf3lKKcfrcQ3Ubw5O6joT0NLgGILQRt9BuyH5AMgEjgFfA0VoI/8tgcRy8mN9/vnn35dStvzwww8fbFCrbhmBq8Wd9CTtP9G8zBy8/G2GNrOrmc5j+vPrtHlltvf088LTZuX07qN4+fuU2YebxZ30pAsO/nzstr4v3kuXR/rTeXQ/7p03jtgRPa/o70o+nW2yuJj0pAvkZebg6u6Kd41qdBkTzxKHY7lafyX4htbA7OJC4vZDhDapQ87FTNa+vZiQprVpfXf3S7k9q+cxwyG3TrYTmw9g8fMmom0MKYdPk3HuIt6BvnR6pP8fitHIZnY102VMPGf3HacovwCA/MwcPP1tNLqrG3s+WVrqnGbqMZZo6g3owIWDp9j21mICm9ah8d3dMLmaaf1of9ZN/dLwHLlY3MnW/RRk5OAZYCujKcjMuWJxD+DqeSmmvIwcLAa+jDQmVzPtxvTnN6cYXT3dyTqjH2M5sblaymqSNu3HFhnELV88g09kIDe9M5ruHz5GzPCuFOVr9+PO14qzzzSnc2Nkax4fx7mDiax6ZxGhTepQ557uCFcz1VtEkaC3OhVm5uBhEHdhZk6p4r4EF6sHtW5tQ/7FrFLDSq4Wd7JKzpN+vg1zUY6modP1Y8fNA5l2HgCZm43Ju1pZjdkFt5tvJ2/hh5e3KRQKhaLCqbI9+DqjpZRrjD4QQgxEG0FPAT4G+kgpi4UQxWjtLoUO8hFovfUAN+oTc1sCc4QQBVLKm/XPXIBMKWU/IUQ34IiU8rBe6Gfr++0HPKZrrUCaECIbrVc/zCnMPH0/VmAA8JWU8qIQIpxL8wSOAf2B/cBJYB7ajYYfcB7tZsCITMBTf+8lDCZ75Gfn2ltA3CzuCCEMbXEP3sqmub+Sm55davviwiI6PdSXuXdNI7hhJNocZacDzM6zt0e4WTwQwmS3Lfzf+8T0aEVAnWBWvbmQW6fcc0V/V/LpbHOzeuDi4YabxQMXN1ciY2PY4HQsV+sPwNNmpVbTOiz8nzZP2z8qhFO7jpCZnMau+Wvp+uRgNn20hPwshzxa3REmYWjr+tRgDq/eza7v1hDaoi71bmpO9YigMvm+lmN2tnV88FY2zP2V8Fb1ECbtOFwtHgiToFZsNBumfWXfT0FWHi4erqU0AQ3D2fv5CrKT09j/3VraPjUId5uVXR//Sr7TNVFCYVYe5hI/Vg8w/fHJRvnZl2Jys3pgfC2X1bR5qA/b5/5KnlOM+dl5uHheis3IX0FWWU2rZwaz5pkPKcjModGomynIziXhsxXEThxOYOsbtH07XCtGMbo6nRsjW3CDCDZ/sZzM5DS2z1/DbY/fhpvNysXdx5BF2p8Fs379XC0F6dlsGfMOrWY9iF/T2lzYfvjScTqdb8NcGGmEICQ2mo0O14+dvFx7i41w9wSDHLt1G0TB6sWQk3VZm0KhUFQkaplMjao8gl8uQojBaJNXbwXOSSnLnXElhPADunOpwF8upewHrAFGOhT3oE2mLWECYNYn8xah3wxJKRcA69Amxn6J1tO/CG1OQA0hhGOT6zy0Hv3HgMPAa7q9J1p/P4Ab2so/F9B67xcAR4GmwBFgXzmHtlX3DdAkNTGljOD07qOEt9QKk6CYcFITUwxttds3pPWd3RgxbyxBMeH0nToSs6uZsFb1OL4pgYunUgiKCSM1MbmcfdTT/WkaI5vZ1Ux4q/pX9HctPk/vPkpxsSS8ZT2CYsJw8XDDFuRH2zu7ce+8cQTFhNNv6qhrinHIG2PY9s0q/MK10yhMwj4iGtyoNhf1PJ/ZfZSwVloeA6PDuZiYYmhz9XAjOzWDsNb1CGkahU9NP7xr+tH6D8ZoZKvTviFt7+xG3bhGBEQF02X6SPxjwjB7uHJWL/RKOLf7KMF6C4Z/TBgZicmkHUvCR+/brtE4koxTKdSKa0jju7rR/6ux+MeEEfvSyFJ+zu8+am/L8Y0JI+uk8bm8GpJ2HyVE9xUQE0aawXVhpAnr0JBmd3bjti/HEhATRvdpI+3aQF3rpx+jMym7ympcPN3xq19Lu+lpVgcppV3r5q31fNe8zHV7avdRIvRzU6Izsp0/fha/UC3ftRrXJjsxhRodG2INr0HrNx7C1iCc6DH9yDpZ9nfaiGZTR+Dftj4Arj6WUjdlybuP2ltuqpeTi/I0wW3qkeR0/ZRQdPKgvS3HFBJJ8YVzZTTm+s1w7dgbzzFTMYfU1iblGtgUCoVCUfFU5RF8iTYqXgp9KczJaKPejv8DugkhZqL3vwsh6qNNwE0GvtBH9wVwsxBiDdpqOF/rI/5zpZRvoBXUU4UQ7wPbAC+0lp8P0EbXEULcgjYin4fW1x8ghLgH7ebgMbS+/v5ANV1zESiUUi4TQmwWQkSg9d+fEUI0l1JuE0IcBm5Ba/OZDywEZusxNBdCeEopnZ/LLwB+Q5voe/O5g4l0fXwQy2Z8bRckLNnKvV8/h3egL3U7N+Hd/s+DpIxt9/fr7NuMmDeWhc/ModWwrvgE+tK4b3tu6NIUhOCTEdO56fFBLHXYx+9LtjDy6/H46P7e6T8eJGVsLW7rUsbfzG5PEhAVQpO+7f6QTyR0uL8XUe0bIswmsi+kM+8/M2l4SxuWzviae+eNY8Ez7+Hu5XnVMQY3isTs7kLNmAgiY2PwrRWASZj477rXSU9K5asHXrfndoSex6jOTZjTT8uts+3CsbP0e+UBqkfWJP3MBQrz8vn6P7No0KsNS2Z8dc0xGtl26efP3cuTpza+QUFmDlG923Bmy0HcfTxx5MgvWxnw7Tisgb6EdW7MN30nIIslXV8eRd1b22J2MfPTAzPtLRsA/b8ay/on55Tyc/LnrfSYPw5LkC/BXRrz20Nv0PSpgeyY/g3XyqFftjLkm3F4BfoS2bkxix9+g/ZPDGTty9+Uq/ms3wQSFq63f37bl2NZ8vQcu/aOb7RjrNWlMcsfeoOWTw5ky0uX/B37ZSt9vrukWXjrBNKPJdFpxn141fInaetBDi9Yb9e2f/Fu2o6/g8AbG/Nm//HU0K/bXx2u231LtnC/fm5u6NyEN/Xz5WyTxZKB0++ncZ9YzK4u7Lr3dXLPpuLi5UnnBc9hcnPB7OlG+v5EGjw9iL3TLu3DiP1vLKb17AdBSpJW7Sbz8Bn7Z0d+2Ur/b8dhCfQlvHNjlvznDVo/OZBNDrlw1nzbdwIAoZ0ac2ZjguE+C3etx/LoS5hs1THHtCD3w2m49b6T/MVz7Zqc156yv/ccM5W8z18v5cPIdrV8NHv6H9pOoVBc/xRX7dUr/zJEyShVVUMIMQiYATgPDfmg9acPQBvF3ySlfESfYDsObQLrVrSe/IlSym8dfDYEnpBS3m2wvzi0CbOH0Qr+73W7FbgP6Ir2xMCCVrzPB55BK7J/AIZKKZOFEHeiFfCr0Pr+T+makmUzg4HJUspPHPY9R/fVG20UvzEwREqZKIR4EfhESvm7QZp80VbsWT0+4o4zBp/j4WOhTlwjjm9KIFOfiGhkKw83HwtRcQ05dhmth4+1jMbIdjn7H/Xp4WMlunsLzK5mEpZuq5AYnT/LPZdejs+rz62RvVBIJ39Xf8yXi7tl+8ac3phgODkWwN1mITSu0WU1zvgYPPN0s1mo2bERSRsSyL1KP86cM1+KKSKuESevEPeVNCUEeFuoFdeIMxsTyClH62a7ssZZu2Hrvitet3XjGnLU6Xw52xxpkXfpfyNXm4XAjo1I3pBA3h/MaQnnXDS/7nrspy9znFejKWF4X30qkqcXLvWbUXRozx+aMOuMx4TZf9qHM67+tSvcp0KhKEOVKqm/CL7jLytsbz/9WZU6dkeqbIF/NQghGgN79NF5F6e++38sQgg3KWX+tWwzPqJyLujiKr8QVOXjIivn99e5wK8ogooqvvPOqMCvCEoK/IrGVknxHnGpeMeOBX5FUlLgVzT2Ar+CUQW+QvGPpUoVuZ8FD/vLCpc7Tn9apY7dkarconNFpJS7HN5fF8U9wLUW9wqFQqFQKBQKRQn/6AJfoVAoFAqFQqEoQfUdaPwjV9FRKBQKhUKhUCgUxqgRfIVCoVAoFArFdYFaRUdDFfgKhULxN/KPe5xs+uc8+C1IOXJl0R9ATd5VKBRVHVXgKxQKhUKhUCiuC9Q32Wr8c4ZiFAqFQqFQKBQKxRVRI/gKhUKhUCgUiuuCf1zbYyWhRvAVCoVCoVAoFIrrCDWCr1AoFAqFQqG4LlCr6GioEXyFQqFQKBQKheI64rop8IUQnvq/hsckhHB3+tnw6YUQIlQIYRJCVBdCBAshgoQQ1oqPuMx+OwohQvT3bkIIob83CSHMlb3/vxJPm5U6HRpi8fX+u0P526isHHjarITGNcTD16tC/SqqNh41qlEjrgEuVo+/OxSFQqFQVAGqbIuOEGIDUAQUOH3kDtwnpdytF8EzpJSPAa8JIb4GYoQQuVLKdx18BQJzgR76z37ASiFEEymldND5AN8DQ4EawK3AEqCnEGK8lDJDCOEGeACxQCdgmh7jKGC1lHK703H0AVKklOv1nz8HRkgp85yOqyewF/gMmAS0EkI0BrYDM4BfyknV+0B0p4f7sWr2AkNB32mjCIgK5uCKnXaNkQ3A6u/D6F+nk3LkDAdX7GTTvGXc/uajzBk8qZTP/tNGERAVwoEVO1ipb+9sc/f25LZZozGZzVSPDCLj3EWObfydiDbR7F+2jZvHDeODoVPIvpBxTX6dbb6hAQREhXB00++Et6hnj9U70JcHFkziwvEk/GvXJO3MBRJ+3XpN8WZdSMfT6gmAh4+VxB2HWPy/DwC4dfqlHK6etcDQ5uFjYcDM/+Bm9cDi50NuWibHN+0nvE19EiowBytnL8AroBoP//R/yLwCzG6ufH7TMxQXFNLjjYcRZhMF2Xn88tAsOr84At+oYI6v2MmWmQvt++005W6Or9jJsaWXLmFPfx9u/eQpfuoxrsx1FfvySGx1gzm1fCe7X19Y5nMAD38fOr07hl/iJxt+XkKP6SPxiwrm6IqdbJhl7Ks8jcXfhwFzn+KTWy7F2PHlkVSLCubk8p1sn2nsz1njZrPQZdZDuFk9ST2QyJpnP7TnIH7Ji2ScOMfOlTtYUc7vGUD8tFHUiAph/4pLOiMbQN/JI9i/cif8uB0Xb0/avj2aatGhmDzcODjnZ35/Zb7hPtz9fYidM4aV/bSc2qJDafbi3Zz7bS+Nn7udZb2eRxYU2fVdXhppP99by8mFs6bB8K5E9Wmr7c/HQtL2w6x69oPScdz+COagUAr3biF/yZfl5kR4V8PzwYlkTx9zWdvVknIhlcfGTmHuWy9f87Z/h1+FQvHXopbJ1KiyI/hSyrZSyvZSys6OL2A/WpEP0NRhk2hgNfAG0F4IYQH7yH4WsEoIESiE8AZuAuZJKaUQwqy/AoH5wFK06yMEGAjcBvgDz+v7qQ08BvQHWgH/BVoDQ4BqQogOQgjHb0FZDzylx9IeSHUs7oUQtwkhEoB2wCghRDIwFu2G4VspZXcpZXnFfTxgBtp5B1bDLyKwjCC6R0tMZhNzBkykRGNkK2Hw7Ecwmc3MGTCRaqH+DHlzDG6WUg8/iOnRCmE28e6ACXgH+lI9IsjQ1qRve9bO+ZFNny6lMK+A1W8uJCAqhLVzfmDVGws5tHoXwQ0jr9mvoy2sZT08bFbmjniJJre2x7PapYctoU2jWPXGQtZ/+AsHV+/i7b7PXXO86Wcu8P0zc/hoyBSOb05g6+fLtbz2bInJZOL9eIe8GtiaxMexa/5aNrz/E2Y3Mz89P5eAqGDWv/djheWgxNZ6aFdO7TrCx20fJevcRSJvasYN/duz472f+P6OaWQnX6T1YwMQZhPf9p+ENdAXm37ua7auhyXAVqq4B2g/biguHm5lrquwm1sizCZ+7jsJS6Av3pFlrz03m4X2rz2Ai9P140zdnpqvL+In4RXoSzWD6/hymk5OMdbt2RJhMvF9Py02H4PYIm4uq6k7oAOHvlvLogGTcfXywL9xJG42Cz0/fQqTqwvf95uEj55rIxr0aIXJbOLtARPsOiMbQESrengF2EhYtg2A8Pj2XNh+iKTVe0j8fgPVm0fhZRC3q81C65mlc+pzQwibH32Hfa98R9bxZKxhNeyf1dbz9p3T+XbESLP3k2UsHDyFhYOncHrTfvbp130JLo1jESYT2a8+ibD5IQKCDXMC4N7vHnB1u6LtakhLz2DsCzPIyc295m3/Dr8KhULxd1FlC3whhM25rcaAJ4EeQojlQEPgR7SR7hDgZyFEDbQC/WfgTuAd4EWgD9BJCLEZ+B1t9LwV8AVacd8NSAXeBZKklMOBeUKIasBBoCPQAAgGOgDewAl9v6P07RFC1AL2AL5CiJXAFKCBEOKiEMJVP4bTwKvAcf0GZijaDcMIwFsIMfQyx98Z+ArgyLp9hLeqV0YQ2TaGPYs34KgxsgFExsZgqebFxdMpABzbkMDuRevJy8xx8hnNnsUb9e336j7L2jZ9upTDa/YQ2TaarAvpZJ5PZ8d3v+Fu9SSidX1qNanDyW0Hr9mvo03KYi6eSkEWF7N0xleYzJcu6dBmUbQedhN9Jo/Aw9vyh+I9sm4vYa3q4R3oi5e/jTN7jgEQ0TaGvT9oOTy6bh9hreoZ2rIvZlC9dk3qdGxMQXY+aafPs/O7NbhZPSosByU2z2pWNs79leA29TC5uWB2d2XP3KWc/G0PAJ5+PtjCa3BokbZd4tq9BLeuh8nFzI3T7iUjMYXI7s3tsYS0i6EwO4/s5LQy11VgbDTHdT9n1u6lhsG1J4skqx+cRUFGTpnPHAmNjWa/fiwn1u2lloGv8jSh7WIocIoxNDaaI7r29Lq9BBn4q2mgyU3NxFanJm4+Fqw1q5N56jyySHJu+2Gyzl4A4PC6vUQY+AOo3Taa3brPEp2RzeRipv/UUVxMTCG6Wwvts4+X4mbz4uT3G3Cv7kPKxv34tzbO6fr7S+f05MINZCemENS1Ka42C5lHz9o/C46NLnW+axr4vJzGGuSLxd9G8u5jpbYx121EwfY1ABQd3IW5doxhTsx1GyPz8pDpFy9ru1rMZhMvT3oWq9Vyzdv+HX4VCsVfT/Ff+KrKVNkCH3gJbaTdiJI50vcDjYH3gAlSypscXh2llOeklB8Bz6CN2PcDXtP9/geYDkyVUv4gpVwM7ARypZSzpZQ/SSmnAIVCiB/0fZiklEXA62jF+uPATLSbg6nAAWAz2o0GQD7ws8FTiB1Aoa7ZCtwOhAshjgPV0Yr8DOBX4JHyErRly5bmsbGxLwkhtuw9dxgvf1sZjavFnfSkVADyMnPw8rcZ2syuZjqP6c+Zfccpyte6ojJT0vDw8izj083iTnrSBYftfUrZmg/qROfR/bh33jjunTeO2BE9Mbu4kLj9kF3fsHdbigqLKC4qvmq/RjZhMiGEIC8zh8zkNMyul7rODqzaybsDJnBgxXYsvt4E1g+9rN8SfENrOMVro/Wd3dj86bLSeT2r5zDDIa+6remgjnR6pD+t7+pOy2FdaXlHV84fOUNOWpZdX1E5cLZF9WlLUV4BHn6X+vuDmkfhbrNSkJVLph5jfmYOnv426g3owIWDp9j21mICm9ah8d3dMLmaaf1of9ZNNW69cLG4k637KcjIwTOg7LVXkJlzxeIewNXT3R5TXkYOFgNfRhqTq5l2Y/rzm1OMrp7uZJ3Rj7Gc2FwtZTVJm/Zjiwzili+ewScykJveGU33Dx8jZnhXivK1X1fna8XZZ5rTOTGyNY+P49zBRFa9s4jQJnWIuqc7oOXUzc8LV5uF1F1HcTeIuzAzh0KDnLpYPQi9tQ35F7NKLQLtanEnq+Q86efbMBflaBre1Y09nywte7BuHsiL5wGQudmYvKuV1ZhdcOt5O3mLPrq87Rrwslrx9qr4KVGV5VehUCj+LqpygZ8PvCCEWOn4QiuA0wCklCWNy88AQ4UQS/XXOiFEdQdfbvrn64H/ofW5dwfqAMccdGfR2ntK/CxF67WvAQyXUl7Qde5o8xdcAE8p5Xg9roH6zcFJXWcCWhocQxBaaw1AW+ADIFOP5Wu0uQd7gJZAYnkJatmy5db169ffK6VsGeMfiT4vt3QSs3Nx1dsX3CzuCCEMbXEP3sqmub+Sk5aJMJkcPit7ieRl59lbItwsHghhKmXb+uVKts5byftDXuDz+18lMyWN1W99X0q/ePxHnNh6kHpdm121XyObLCzGxdXV/vmlez84sfUA+Vm55GXnkXbmPNUjgi7rF7RJqrWa1ikdr8lERLsYjq3fdymvWQ45tLojTKKUbfuXK9k2bwWpJ84xq9PjbPjgZ9LPXqDZoI52fUXlwNm2etzHpB9Lwq9uCADu1ax0nHwny594l4KsPFw8tHy5WjwQJkFAw3D2fr6C7OQ09n+3lpB2MbR4qA+7Pv6V/PTsMucfoDArD3OJH6sHmP74umT52ZdicrN6lHMdl9W0eagP2+f+Sp5TjPnZebh4XorNyF9BVllNq2cGs+aZD1nQazy73/6RQ/PX8cOgKex5/xeEfnyO14pRjK5O58TIFtwggs1fLCczOY3t89cQ0F4b/S4uLKL+I33Z8th7uOjXyNVSkJ7N5jHvUJxXgF/TSx2CRufbMBdGGiEIiY3m9Prfy+4wLxfctOMSbh5gkGO3bgMp+G0x5GRd1qZQKBQViRR/3asqU5ULfIDRzqPfUso+UsoDDprngMPARinlTWgj3luklOcdNIFoI+wPoBXRL6JNuG0O7HLQuQCZup9pwP36+zQgG0AI0Q+tB38C2lOG/+oTaYOBMKf489CK9AHA50A/fQS/B5ee7hxDewKwH20OwDy04t8POM/l2YrWIkRQTDipiSllBKd3HyW85Q04aoxstds3pPWd3YiKa0RAVDB9p47UP0sux2c9ffswUhOTDW1mVzND3hjDtm9W4Reu9f62Hn4TPsHavZeHj4Vch+Lsav062ooKC3H39rR/XpiXb/d399xn8AqoRlLCCWrHNuDcgcRrjjcoJgwXd1dObT9UKgdndh8lrJWWw8DocC4mphjaXD3cqFE/lDN7jxHeJhopoeXwm/CpWXE5KLF5B9hoN6InAN4h/mQkJmNyNdPzzdGsn/oVGafOc273UYL1Fgz/mDAyEpNJO5aEj963XaNxJBmnUqgV15DGd3Wj/1dj8Y8JI/alkaWO//zuo/a2HN+YMLJOlr1Orpak3UcJ0X0FxISRZnDNGWnCOjSk2Z3duO3LsQTEhNF92ki7NlDX+unH6EzKrrIaF093/OrX0m56mtWhZP59yq6juOktXjX1XBtxavdRIvRzUqIzsp0/fha/UC3ftRrXJjsxBeFqxr91PVI2JJCdmIItJpysk2V/n41oPnUE/m3rA+DqYyl1U5a8+6i95aZ6ObkoTxPcph5J2w8b7rPo5CF7W44pJJLiC+fKaMw3NMU1rjeeo/8Pc0gk7rePNrQpFAqFouKpsqvoXA4hxGC0ybDDgEZoBfRkIcRjaJNV73HaJBb4FPBCK+AvCCHy0UbfHf8XdXV4PwEYoS9RWYSeKynlAiFER2AF0AIokFIuEkJ8AdQQQgRKKZN0H/PQbgK6AMvR2oPu5lLP/0i0pwtRwBq03vsFaDckTYHdOA5Jl2UB8BsQ3LBXG74aPZuujw9i2Yyv7YKEJVu592ttcmndzk14t//zIClj2/39OgDcvTx5YuNs8jJzaNirDV+OnoVvrYBSO/19yRZGfj0eH337d/qPB0kZW4vbuhDcKBKzuws1YyKIjI2hWrA/JpOJB7+fDELww4SPr9mvoy0gKoTiwiJuHjeMup2bUJCdy02PD2LpjK9Z/vp33DtvHMWFRRQXF9NqaNdrjtcvrAaJmw/gbivdm5uwZCsj9BxGdW7CnH5aXp1tF46dpe/L91MtxB8pJYHRodgqOAcltmOb9zPm1+nU3jQTF093lj/9Pr3ef4yARpG0HN2XlqP7su/rVdSLb4810Jewzo35pu8EZLGk68ujqHtrW8wuZn56YKa9ZQOg/1djWf/knFLHf/LnrfSYPw5LkC/BXRrz20Nv0PSpgeyY/s1lLldjDv2ylSHfjMMr0JfIzo1Z/PAbtH9iIGtf/qZczWf9JpCwcL3989u+HMuSp+fYtXd8Mw5roC+1ujRm+UNv0PLJgWx56ZK/Y79spc93lzQLb51A+rEkOs24D69a/iRtPcjhBevt2vYv3k3b8XcQeGNj3uw/nhpRITTp245fHX7X9i3Zwv36ObmhcxPe1M+Ts00WSwZOv5/GfWIxu7qw897Xiby9M55BvoTFt6dm16YgBGuGvUSDpwexd9qlfRiR8MZi2sx+ECklSat2k3n4jP2zI79spf+347AE+hLeuTFL/vMGrZ8cyCaHXDhrvu07AYDQTo05szHBcJ+Fu9djGTMdk6065ugW5H40Hbdew8j/4VO7JmfmM/b3nqP/j7wvZpXyYWRTKBSKP0tV743/qxAOq0RWKYQQs4AlUspFTnYBJAD3oRX2j6EvnQk8gVbwPiWlPKHrPdFW12kLDEIrqH9EK75dgEFSymRd64I24j4KbcT+Q7Te+w+AD6SUOUKIW4C+wLdoRXoA2lOAW4AX0Eb++wPVgLfRlrzsI6V8Xl+G0w/oCkQA86WU24QQs4FmaG0+zwHbgNnAJ2j9+IOklOU1M/sC3aa3eujLTIPJkKCNEteJa8TxTQmUaIxsRvr05Ivl+LQSFdeQY6V8lrVdTv9n/DraCvMKr+j7z8Sbey69HJ9/PK+ZyWkUCuPfvT+SA0dby/aNOb0xwXByLIC7zUJoXKPLapzxMfiL6WazULNjI5I2JJB7lX6cOWe+FFNEXCNOXiHuK2lKCPC2UCuuEWc2JpBTjtbNdmWNs3bD1n1XvH7rxjXkqNM5cbY50jzv0j28q81CYMdGJG9IIO8P5rSEZBfNr7se++nLHOfVaEoY3v+i9sbTikv9ZhQd2oPMuPinYgXwGD/zT/v4q3D1r31lkULx76FKNau8GTrsLytsHzr5aZU6dkeqcoE/CG39d+dnvz7AHillvF6Qf4fWajQPbRWc7sCDaO0yNwH3orXYHEBr05mLNvL/MNqI/XtoNwk5aBNmDwNzpZTf63FY0W4euqKti29BK97no/X+/wb8AAyVUiYLIe5Em0C7CggHTumaI3r8wcBkKeUnDsc6R/fVG20UvzEwREqZKIR4EfhESmnQCHuJ8RF3VMqJLKZqXh9/JS6V1GhXXoH/ZwkqqvjOO6MCvyIoKfArGlslxXvEpeIdOxb4FUlJgV/R2Av8CkYV+ArFP5YqVeTO/gsL/IdVgV95CCE8jUa3hRAmKWWxfhNQVPKFVvo6+Pkla9Hr6+ULKWWVmfUlhHCTUuZfWXkJVeBXHqrAVwV+CarAVwU+qAJfoXCiShW5qsDX+Ef24DtSXuuKlLJY/7fQyZ7h9LPxMiF/I9da3CsUCoVCoVAoUMOSOlV9FR2FQqFQKBQKhUJxDfzjR/AVCoVCoVAoFAqA4irbNPPXokbwFQqFQqFQKBSK6wg1gn+dUFmTYSurl81LVvy9ZU4lTVqtpHmg5FWSX69KmGCaWUlDAcmmokrxa660s1bxRHv+ueUwy6NuJUy2Bvj1K59K8Xtq/vhK8VsZPLB9UqX4VZN3FYo/j1oHX0ON4CsUCoVCoVAoFNcRqsBXKBQKhUKhUCiuI1SLjkKhUCgUCoXiukC16GioEXyFQqFQKBQKheI6Qo3gKxQKhUKhUCiuC9QXXWlcNyP4QggfIYRnBfj50z7+xL47CiFC9PduQgihvzcJIf45y4IoFAqFQqFQKP42rpsCH5gAvCSE6Oz06lEiEEL8LoRY6fQ6K4Ro7ODnUyFEFyHEOCHEMiHEUiHEav3f34QQNfXi20cI0UMI8aIQwiaEsAghxgghmjkHJoToI4SIdfj5cyGEu8Ex9AQ66+8nAUuFEMnAz8BNfz5FVQ8Pm5WIDg3x9PX6u0NRKBQOmG1eeHVoitnXu0L9etSoRkBcQ1ysHhXqtyqSciGVOx984u8OQ6H4V1Es/rpXVea6aNHRi+cYYB4Q4fSxq8P7JCllZ6dtPwKyHEzjgHeklB2BF4QQA4AuUsqHHbapDwwBgoA6wH+BlbptlxCiA3BaSnlE32Q98B7QXwjRHkiVUuY5+LsNmAicBdoJIV7TfYcDT0kpH7iaPPSfNoqAqBAOrNjBytkLrklnZBv0+n+od2Mzfntn8Z/25+7tyZBZozGZzVSPDCL73EVObEwgrE19Di3bTtfn7uDz218k50IGALdMH0n1qGAOr9jJulkLcff2pO/shzGZTeRn5bHg4Vl4B/nRfdJdBDWKQBZLNn2ylFXlxNl32igCooI5uGKnXeNsazWsKw17twXAw8dK4o5DnN13nM6P9MfV052C7HwOLN3Kj//7oIz/3tNH4R8VzKEVO1kzS/Nv9fdh4Ftj+HjQZACqhQbQe9pIatQLpTC/gI2fL2fFZfIaP20UNaJC2L9ih11nZAPoO3kE+1fuJKZ7C8Lr1MJ2QzCpe04AsOG5j7mYkGjXtnt5JNXqBpO4fCe7Xl+IV2gAbabchZuXJ8k7DrNl0ud2bdsX7yZxxU4OLNtut3V5aSS+UcEcX7GTrTMXGsburGkwvCtRfbTcuvtYSNp+mFXPankcOO0+akSFkLBiB8tnzy83H0Y6L38bw958lLcHTyyl7f3OGELbxbD13R/ZNMs4xm7TR+IXFczRFTvZNGshwmzinjWvkHYiGYAV4z8m9fAZer72AO42KyYXMx8++Bq3jB1qeA6cuZrz12bYTUT2bgWAyceKyd2VoouZZKzcQvIbX5bxafK2EPr6UwgXM8VZuZx8ZBqyuJgbVs2h4EQSAKcnvk3e/uP2bUKnj8YjqhbpK7aSNOsrQ58Rs59EmM0UZeVw/OGXMPt6E/nOs6Qv20zIc/dw6PZxFF1IL7Vd81dG4V03hLPLdrD/NeM8uPv70GbOo6zup60b7xMdStMX7+bcmr00eu52VvQajywo/V0IN76knZdjK3aypZzrqzxNpyl3c3zFTo4t3V7hvjz9fbjlnTF8O2CyoR9n0tIzGPvCDHJyc69Kr1AoFBXJP34EXwhRF3gR+A4YDgxzeN0NLHCQ+ziP4KONmtv/h5FS/g501X17AlPQRtL9HfwcBDoCDYBgoAPgDZwAQoBRQDfdRy1gD+Cr728K0EAIcVEIUXLzcRp4FTiu34AMBVoDIwBvIcTQK+UhpkcrhNnEuwMm4B3oS/WIoKvWGdma9O9ARJtoLhxPwucK/kz6tj4O/pxtTfu2Z82cH9n46VIK8wpY98Yi/KNC2Pjej6yb/T1HV+8iqGEEADf0bIkwmfgkfhLegb74RgTSoF97Nr33E/OGTSMr+SK1OzWhyzNDOLkpgSMrd3Fy036CG0fiFxFYJsboHi0xmU3MGTAR78Bq+EUEGto2f7qMD4dM4cMhUzi+KYEtny8nMzmNI6t281LDUWSnpnNo5c4y/uv3bInJZOKj+Eu+PHws3DrjAVwtlx7UtLq7O0fX7OXQip2knTqPX1iNcvPaQM/h2w45NLIBRLSqh1eADbOLGZPZxIaxH5N5PJm1T83h50FTShX3YTdrx/1j30lYAn3xjgykxdgh7HptPj/FT8Za04+g2GgAarSuh2eAjcRfLxX3tXu2RJhNfNd/EtZAX2wG+TbS7P1kGQsHT2Hh4Cmc3rSffZ8vtx+nMJt4c8Dzl73OjHSePv/P3nnHR1G8f/w9d6l3SS69kh4ICQm9BCH0JqBAqCJgRUVR7BUBQfyKFQT9WlBEUcEGKFjoIEV6DYSShBJqAiEhvc3vj92ES+4CARJ/yHffvu4lN/vMZ595ZnYzOzszZ2TIu2OwM9hb2Po1iyB9/3GcfNxwteJjhOrjgoTJFTZeUUEc/GUTPw6dyo9Dp3L+YBohnZtwdO1eFo58i2Pr9tL7leFW6+B662/zvBWkDn+J1OEvUXL2PIVHT5Ey+DlsfNyxC/G30HXt14mMLxZxdNSrFGdk4tShOQ4NQ8n6dV2Fjnnn3tSrLUKn43DCC9j6uGMX4meh6da/I+c+W0zyiAmUpF/EuWNzHOoHcXLy55yd9QOX1u3EEBNeKY9/b6U+1t4xCUdfN4yhlnGwNRlp+cEYbMzqx6VBPbY/+QlJ7/5M7vFzGIO8K+UJ76W0zx+v0L6qs/FvHYnRy1TRIa9NLXuTge7vP4KNo7UXr9bR63W8M/kljEZDjfNoaGjcOGX/4Odm5l/fwQeOAUMAH2Ac8BuwVErZTT2WY2Z7QUrZyfyDMv0FACHEcLUTfp86//0jIF89PF8IEQ8gpSwFZqB01p8BPgBaAW8Ch4Ctqh8ARcAfVs67CyhRbbYDdwHBQohjgAdKJ/8SsBx44mpBCI2LYt+SzQCkbEwkuFVkje2spQW3bMBvk7+iICef5Kvo7VXzJpvpVU3bPG8Fyev3ERoXRe6FbPLOZ7H357+wMzoS2DoSvybhnNxxBIDguCiSlir5j25MpF6rSHZ8vYKj6/cBYPBwIe98Fu6hvjj7uZO0dDN557PJSD5t1c/QuGj2LflbLd9+1UfLtHKcfdxw8jRxet9RQuOi2b/0b5x93CjKLcDRZLTQD1ZtFH/3E9gqkrIyyc9jZ1J4Kb/CLj8zh5B2jUj5aw96WxsOrdtDSDVxDasSw5BWkVbTdDZ6Brw5motpGbQY0pG9Szbj1SICB28T3b56lviZYxD6y5e5b9soUn9VNE5vSMSnVSSmMF/O7z0KQEFGNrbOjggbPe3efoCctAwCezSvyO/fNoojav60DYn4tbb0/0o2Rl83DJ4m0tXzhcdFs0eth+SN+witJh7W7MrKyvh27AwKcvIr2bYd2Z1jf+0F4MTGRPytaNZrG8UhNZblNr7NIojo2ZIhP71KrxlK3FKW72D/D+sAcHR3xsXH3aIOrFHT+ivHxscDuxB/LnzzOwC5m/ZgaBltoXth3m/krt+l5HE3UXI+C0OzSFx6tCX0+2nUe/9ZMKtvp7gYMpeuB+DSxj04tbLUPP/17+SUa3q4UHI+i5wNu8nbeRBj60YYmjQgd0dSpTyet0WR9otSlnPrE/FsYxkHWVrG5oc/oNisftIWbyIvLQPfbk2xNRnJST1TKU9A2ygOm7Udfyvty5qNzkZPl2kPkJ2WQajaXmtTq6xU8sejMymq0tauhJPRiLOT5f1CQ0ND45/gVpii4wfMAcJQRt4LgQB17n0T4DchxDtSyqXAFYdSpJTfCiFOAXcAX6GMvJcP2d6tag2RUiYD9lwe+XeUUk4QQnQDekgpnzeT1QEt1QcHc3wBPUonPw74AuVB5Sjwg5q2D+gNpGHJJ8ePH+905swZf4C29/Vi+/fKKQpz8vGwMloFYGewJ/vshUp2dgZ7mg/pSPwjfXE0GbEzOuAe5MOWeSuuW69qWjlugd7obWw4tTOZUCdH3IJ98WscSllJKbJUeR62Ndhz6UwmAEWX8nELvjw6GNA8AgeTkVM7k0n6fQsNe7fmQsoZwjo25o+p3+BVP8DCR1uDPdlnMyv5Yy2tnDajurP1m5WVfGk5qjtJf27D6Gmyql/ub+GlfNyCfSo6Ah5hfoyc/4oSK6MD/o3DyDpxjmOb9lOQlYt7oJfVuNoa7MmqEkNrac0T4jl3OI21n/zKfXNfJKR1JBlLtrN10jc4B3ph8HenXpemnFi+AwAbgz15qq/Fl/JxCfHl6NItNHk6gfQdRwjo3Jjt/1lAxKD2XDx0kn0fLaHh/T2wq+fB3i+XY2uwJ7c8f04+Jisj2Fey6T3nGYQQ9PteiUlA2yi2fr8agIKc/GpHxM3bVLldoZXOlsHVCZ/69fj1jQV0ePVuCi/l42rNR0d7cszqzDXEl+PrE/lhyFRyz12k85R7CO3SlNAuTXEL88PGwRaP+vWwc3Lgj2nfVaqDG6m/cjxG9qHo6ClKzpwHoOxSHnbBlqPt5Tg2a4je5ET+roNQVkbqsBcpSc/E77VHcO7UkksrtwCgMzhQbKZpfwVNQ/NI9CYn8nYerEhzvaM9sqQESiuPVdkY7Ck4rZSl5FI+TlbiUFJNZ9jG6EDAnXEUXcy12O7CxqztFOVYrztrNg0HtufC4ZPs+O8SGt/XA2d/j1rV2vPlcqtl0dDQuPnQdtFR+NeP4Espj0kpu6DMge8OnATGSCl7ARuklJ3Vzj1AkLpYtuID9LAiWwzMlVJOMzvPWZRR9fNCiP7A06gLe4GnhBB3oEzXCaqiVQi0BAYC3wL91RH8nlx+w3MU5Q3AQWAhyloCPeAOnK+m6A8HBQVFtm7d2rl169bOG+f8gY2DHQB2BgeEsF61hXmFFnaFeYWs/2wpnw97nVXTf2b7/DXUVK8orxDbKnbW0gAcTUYCm4az9r+/AGBrdEDoBMtencvJ7YeJ6NpU0cwtxMbBtpINKAtyu782iqXPfQrAxpmLuZB6hoa9W7P3p78Qeh3qxkNVfCww88ceIYTVNAAhBKFto0ndtL8ir42jHSG3RXPx2NkKXyrp5xZcjpXRvpLN+ZTTfD1sKl8Pm0rO2Uw2f/EHOxesxcbBjuCWDW44rv6NQtj6nTKV6EzScQKbRZB54DhlxSWgE2QdOYVL2OXOV4lZbG2MDqAT7JmxmJOrdtPgrk4c+eEvSvIKcY8J5tA3q8lPzyLlpw0E3KaM/Bab143BwWo8qrURgtLCYr7v9UrFdJ31c36vUibrq5YKK9VX9Xa3v3AXqVsOoLPVq/XhAFZsi/Mu+1huk5F0nNxzFwHITD6Fa4gPK1/6goUjp1FaVMKPw6ayYc4fVtt2Va7lukAIjHGNKTx6GqEe1xmtxxaUxa/+kx7m5PPTAShISqUkXX1YSU7DLuTyQ25Zbj46B3tV0xF01v3Vm5yo99pDHH/ug0rpJ1/9hNztSbh0bVUpvSS3EJ3qq43RAVGNrjWKs/PY/sTHlBUU4dY0rPKx3EL0Fc3KrQABAABJREFU5fVyhfZV1cYrJph9364mLz2Lgz9voN5t0bWqpaGhofFv41/fwTfDVkpZDLwM3F6eWD7PXQgRCuyUUnYz/wDLqujYAEgpV5TrmqUdlFJelFIuAjaiLIxdAPwqpfxVPa+3EMJ8OGs+yhz9p4FkYLqa3gv4VP23HRABXECZe78ISAWaAinA/qsV/tTeVIJbKq+gfaODyExLr7Hd1dL8rqB30oqdtTS9rZ5hH45jx49r8QhWwtN8ZDdc/NwBZeFlQXYeAGf2plJPnb7gExVEVlo6Ols9/T96nDXTvif75OVnntS/9uIa7M2Wz37HNzqYzLSMasrcQC2fYmMtDSC4dSRpu45Uyts4oT0ndx7BJyqYLCv6Z/amEtiqgeqvdRsAJx83LqalExwXhW9MCG6B3leMa4iVuFZNO3/sDO6Byjzm4vwidDod8R+Mwb9jLLknMwi+vSUX9h+v0D2/NxVvNbbu0UHknFDOfyHxGMYADxI/UaaIXDp6FqdgRdejSSiXTiplSt+bWjHlxiM6iEtW/K/Oxr9NJGd3JldbTv/o4BrF40p2oW2i8I0K5vYZj+IVHUSzB3qRbcX27N5UAtQ4eEUHkZ2WTq/pY/CMCkLoBOE9W5JxQIlbj3ceIvH7dZzdk2q1Dq7m75XqD8DQqhF5uw9SsO9IxbQch6hQitLOWegKWxsCZ73ImbfnUnxKyV/v3WdwaBgKOh0uPdpSkJRSYZ+3NxljK2VNhWNUCEVpZ61qhnz0PKemfU3xSUXT+5EE3BI6A6B3MVKanVspz8U9qRXTckyNgsg9YT0OVWk67X484hoCyhz9YvWaLyd9b2rFVBpPtV6qYs3m4tGzmNT5/N6NQ8k+mVGrWhoaGv8eypD/2Odm5lbq4GeqI/LzgNbqvzujdLABHgJ+spLPBvWNjrrX/ETgaSHEC0IIE8r8+oqtFYQQTkKI3oARZXS+GPASQtyP8jDwNPCVum2mv2pzESiRUq4EnhBChKBMzTkthGgupUxC6fz3BpqjzNtfjLKItxhofrX9+Q8s20bThPbcPn4EMX3iOLh6J14RAXR7ZvBV7a6U5h7sUyt6LYZ2JiA2lMAWDejy5EAGf/EMLv4e+DQK5tGN09HpdaSuU+ZNH1q2nZgB7ej66t007NuG5FW7aDK0E76xodw2th/D579CVN82ALgFeSNLy+j43GBi+rTh3OE0ulbxMWnZdpoktKfX+LuJ6dOGQ6t3Wk0DiOjQmKObkyrljbq9NW7B3kT3bUP6oTQ6PWup33hAe7q/ejfRfdtweNVOrLH2/Z9ofV8vOj83BIObM/Uah5G0eifeEQF0r+Lz/mXbaJbQnj7jRxDbJ46k1Tutpm1dsIawto14aMGr+DcKweDuTGlBEfWHdKDpUwnobG04/Vdihe7xP7YTPqgdrSbeTcgdbUhbuQuAmDF9SPz0d0oLipQ6+G4tfrdF0eun8TS8pxu7PlGWlKT8uZ0GCe24bcLdRPRtw4WDJ2n93KBKvle1OaaeI7BjY05vrjyXO3HZNpolxNN3/Agaq2XyjgigxzNDrmpnjXe6PM2H/V+lOK+A4twCHN2dOX/oJLc9W9nH5D+3E5XQjg6v3k39Pm1IXbWLv6cvpNf0Rxjxxxuc3nGE4+sTCenUmIheLYkeFM+gBa/g7OVqUQc3Un8Azh2ak7dlH9nLN+E6oAu+rzyIS+94Cg8dx/vpEZV03YZ0xzEmAu9HhxL67X9w6RPPuZnzqffe00Qs/YC8nUnkbri8EDxr2d+4D+iM/6v349q3PQWHjuP77N2VNN2HdscxNgLfsYOJmD8V177tOf/tn7gndCbi+/8g9Dourasc71O/byNwUHtiJ40g4I44Lh1MI/qFyjGwxqEPf6XRS0PpsGgCmTuTyUk+bVEvDRPa0d6sfcU9Z1l35jZHV+5i//y1BLSNIuHH8cSO6sbOT36rVa0b4ctZb91Qfg0NDY3rQUh5cz+BXA9CCBvge+CElHKcOnr/IxCnjvKj7kO/Qc3STkpZKIQYgLLV5USUB4KOKB1xe8AJ8AbeVT+uKNNpXgT+ApYCw6WU6UKIUWq+tShbXZ5UbcqH1vyBKVLKr818nq1q9UUZxW8MDJNSpgkh3gC+Vnf4scr4kOHSwcVIRHwMR7ckkZOeVW18rNlVlxZei3rm9jHtG3NiSxK51eg6uBgIiY+9ok1V28NbD1Trp4OLgfD4WI5V8tEyzRrOzgbCVLsr+Xs1m6q2SVfwV7EzUj8+htQqca2aVjVPx9sac/bvJPKr0bYzGfDvEHtFm6rkmQ0F2JsM1IuP5dTm6vPXxAbgmL4URxcj9eNjSdly5XjU1A4g2NmZ4PhY0jYnkXcFH69mU5VzurKr1kE5Na2/u3WXt5/UuRhxat+MvC37KMm4WCOfqqOkVKk0vYsR5/im5GxJpCT9xjQBUgqV36ywNRnx7hhDxqYkCmsYvytx0lbx195kIFBtO1equ6vZ1LaWOY/snFwju2vF1jPs6kYaGjcfN9WO8FOC7/7HOravHvvmpiq7ObdkBx+UX7aVUmabfS+fwmNuo5NSllVJszffo94sXQ/IqvZ1hRDCTkpZVFP78SHD66Qi66p1OMnaf3mUL+rGW9s6CkJeHfkbWVz7sc2ro3d9x/SlVze6DnzL6uaHn8/pav/yN+/g1yblHfzapryDX9uUd/D/DWgdfA2NStxUnVytg69wK+yiYxXzzr36vdiKjcVfa2udezW9bnoi1XAtnXsNDQ0NDQ0NDQ1tF51y/j1DJhoaGhoaGhoaGhoaV0Xr4GtoaGhoaGhoaGjcQtyyU3Q0NDQ0NDQ0NDT+t/hHFkr+C9BG8DU0NDQ0NDQ0NDRuIbQRfI3/F3LFv+cZu6CO1sjX1dP1TtuSWte0r6NNEkJK62a3m+I6qrPSOli+9U2ZS61rAlTzQ7g3TLFD3Sxhc/gXLY2b1HJ83ehue71OdLXdeTT+lyi7afe1+WfRRvA1NDQ0NDQ0NDQ06gAhxOdCiI1CiGpHBmpic61oHXwNDQ0NDQ0NDY1bgjLkP/a5GkKIBEAvpbwN8BdC1L8em+tB6+BraGhoaGhoaGhoXCNCiIeEENvMPg9VMekEfK/+exXQ3opMTWyuGW0OvoaGhoaGhoaGxi3BP7maR0r5KfDpFUyMwEn139lAxHXaXDPaCL6GhoaGhoaGhoZG7ZMDOKr/dsJ6v7smNtdMrXfwhRCBQgidEMJDCOEvhPAVQhhvQMu2lvxyvLpVpfPWShmuBSFEP2vlFUI0EUJ41/X5NTQ0NDQ0NDT+zZT9g58asJ3LU26aAEev0+aaqdUOvhDCBfgFiARigGdQnH1dCOEshLATQrgIIXoKId4QQpiEEAYhxDghRDMrkt8CV9xDTgjxtxBigxBiTZXPJiFErJnpPCFEZyHEeCHESiHECiHEOvX/fwkh/GpSBtWmxuUQQuyp8n1/NeXwAl4BnhBCrBJCfC2E6KIeHgaEXikON4qjyUh4+xgMbs5XTNO49TGYjES2j8Wo1buGxj+Go8mIsHUEcfU/yxkXMhk15tl/wCsNDY0bZBEwUgjxHjAESBRCVN0Pt6rN0to4ca3NwRdC+KB0yFegPNgEAIMAN8AWmAjMRums+gLhwFPAGjVtjxCiPXBKSpmiynoCC4Sw2NTUEegspSySUsZV48+XgL1Z0njgEyllB5TO+kBVY+w1luFZIOwaypFfxbUc9Vx64H5gtpRSAg8B30gpZwgh+kgpRwohQoUQ3wEHgVIrxfwciAJ+sxYDgAHTRuMVEcCh1btYM2uR1TQnL1eGf/IUB1fu4PbxI/hi+FT6TLyHhl2bcWLnEZy8THw+fCp5Fy5dl77BzYnnN82ipKiE4vwCvhw1jYzkUwx6/1EcTUb0Nnq+fWQ6je9sS2xfpTodXAyk7Upm8cufV1c0i/NU/a7T63h63XT0tnpsHe3Z+dNfLJ00t8Z+6/Q6XtkzG1lWRt7FHOY9+C4ZKacY/P5jGD1dyL+Yw/yxMykrKa2Rnr2zI8NmPo5vwyBs7O3Y9OUfrJrxMwB3TrmPQ2t2k7RyxzXFthyjpwv3zn2RD/u8jE6v4809X1BWVkbuxRxmP/g2pw+esBrDu6Y9jE9EAPtX72TZrIW4eLly/ydPk7hyB/3Hj2TW8Ck07R1Hi75tAfCNDKSspJQNc5exatbCautm0LSH8I4IIGn1rgo7J08TIz96kv8OeQ2AgEYhjPr8eeycHDi99RBLR71tVavz2w/iFuHPsdW72f7BYhqN7ErEHUo7sXcxcHZnMmtf+gJHTxd6fjKORQOn0O0tJc/R1bvZOnOxVd2qNkKv477175F1PB2ANRPm4hriS9P7ewKgt7clOyeXrNMXLMp2vTEYNO0hfCICcDQZmT9uFqcTj1nVSpg2Gu+IAA6u3sVqtd6rprnV8+LOyfdi7+SIo8lIYU5+RTux1r6gZm2svN3q9HqK8gr4euwMBrz+AN4RAej0OlbOXMiBKro1jYH58ZAm4aya/hNnqsSg37TReEX4c3j1btaqPlZN0+l1PLnufTJPnAMg/2IuTl4mDq/ezYbPljJ22TSmd3z6hnWXTvyKc+q1NPCdh4nt25Y1sxZVuharUpMYO6v3YGFjj97oQUnWKZDWxwezsi/xyuvvkl9QUO05NTT+l6nJ7jb/FFLKbCFEJ6A78JaU8gyw+yo2WbVx7tocwW8FfIfSMe4OZKIsPDgrpRwJzAfSgQ5AI8Af5ZWEM3AcpTM9Ws2LUHr1GVLKblY+7aSURaqdSQhh3pG3ipTyANBVzeMITAVWCCE8r6UMQghX4HBNy4Hleo8yIUQUsAyIB4xCCA+UNwVZQoggoIEQYi4QDBRWU6QEQA/cBvh7hPhaGET3bIXQ6/h04CScfdzwCPG1mubToB6/TfmatR8u5si6PcTd2xODmxNzRv6H80fPcGJXMv4xli8Qonu2QqdquZjpV03r8PAdHFm/l9cbP0jKpgPY2NnQoFMTjqzbw9xRb3J43R6aJrRny7wVfD7sdT4f9jpHtxxk67erqim6ZdnajOxuWa6GQZzcl8rhdXt4PfZBdHpdtTGwltb2/tvJTDvHlNgHObxmNyWFxdTv2ITT+4/x+bDXST9yiqjuLWoch6b92nFiVzJH1u9l79K/qdc0Ao8QX4JbReLkZarU+aqpZjm3v3w3tg52AHS8/3bOp53jxdj7SVqzi5LCYqsxbKyWefrACZh83PAK8cW3QT0WTvmK5R8uImndHgJjQtkwbzmfDJvChjl/kHM+my/uewsXHzc8rbQ5gBjVz48GTqywc3QxMvTdMdgZLl+qd80Yy5lth5gdNRqvRsH4t42y0Arr1RKh1/HzgMkYfdwwhfiQ+PVKFg+ZyuIhUzm15SD7v12FvclA1/cfwdbRviLPDwmTcfJxwzXEx0I33IqNZ1QQB3/ZxE9Dp/LT0KmcP5hG8p/bKr4f+X0rp/cfsyjb9cag3Obvr5ZTWlxCUa71S72RavexWb1bS+v14l2smrmQDZ//jp3BnuVvL8DFx43YO9patC+oeRtr2q8d62f/xpyR/+FSehbdnxqMTq/jtze/xejuTHryqeuKgfnx8Lgo8rNyLDr3UT1botPrmD3wNZx9XHEP8bGa5tMwiL2/bmLOsKn8PedPCnPyK473Gn83zj5uN6w7Z9jUis59dM9W+DdS7olVr8XribG3eg8uy79IWVEewqb6P2l6vY53Jr+E0Wio1kZDQ+PmQUqZKaX8Xu3cX7fNtVJrHXwp5RKUp5ICKeUsKeXvUsqpQIkQYinwGUpndwZK5/oZ4AOUTvWbwCFgK5dHo31RHgiuxttAt2qOCQAhxHAhxBrgPvXB4SMuj6zPF0LEX0MZdFLK0msoh6MQYr0QYrsQYizgB7wDvCSlHCWlzEF5ICi3f1DVPMGV66cTZtsqBbeKtDAIjYti35LNAKRsTCS4VaTVtOQN+0jbeYSQ1g2p1yQcJw8XNn3xB2k7j3Dp3EVCWkVyYsdhq/p7Va1kM/2qaQYPZ4JbNODhn18jon0M2WczSVqxgx0/rAXA6O5M7vnsCl1nHzecPE2c2peKrYMdwz4ax4MLXqXv5HurLVtUj5YW5QpsFkFYXDQBjcMZPP0xUv/eX20MrKVFdmmKo4sTo3+YiHdkICGtG5KXmYNHqC92Bnu8G9Tj/NEzNY7D5nkrcDQZ2btkM0Z3Z45tTSKkTUMGvDmazLQMorq3uObYAoS1jaY4v5BL6RcBaNSlOQYXJ8b9MAm/yEDCW1t2nAEi4qLZtWQTAIc2JhLWKpJDG/ZxbOcRwls3JKhJOKlm9d6wSzNyL1zi5L5UkjfuI8RKmwMIi4tmz5K/VT8Vu7KyMr4ZO4OCnMsvtJw8TSR9vw6AnNMX8G9tqeffNoojvyplTtuQiJ+ZjdHXDYOnifS9RykrlSx7dCZFOfn4t43isBqnExsT8bfiZz0rNn7NIgjv2ZLBP71KzxljEPrLl5/e3pbgDjHY2NtZlO16YxAWF03S6l30Hn836cmnaDWsczVales9pFWk1TTPMF9O7UslLC6KM0kncHA2kLr5AL1fHWHRvqDmbWzzvBUkr9+nxNzdGY8gb/b+voVBb47m7KE0brun53XFoPy4o8lIvdgwHJyNhLaNruJjNPtUjZSN5devZVq9ZhFE9WjJAz9MoNvzQ9n/+xYAMlJOE9i8Pmm7jtyw7sDpj6JT20R0z5YUFRRyYteRStdiVWoa4+QN+zix8wjCxgFha48sqX503sloxNmpzpeEaWj8a5H/4Odmpra3yTwDtBNCrKiSbgJGSikvqKPt5dNNHKWUE4QQ3YAeUsrnq+RxsaJlC9hIKdup34tQptw8V8XuEpAFIKX8VghxCrgD+ArYx+VXJHcDvwkhhkgpk2tSBvV7TcuRL6Ws2NNUCDFCStmnivY0YCAQBHRRtdsAc6ge44gRI+K++eabyQMGDHC5o2kXCwM7gz3ZZxV3C3Py8QjxsZrW740H8AzzwyPEF1tHe0LaRLFlnlL8gMZh6PQ6ykotXxdXp99iSEfiH+mLo8mIvdGBI+v3kbo5idP7jxLbty31mkWQ9Oc2ANwCvQm7rRHL3lpQoRs3qgdbvlHO33J4F84dSiPf25XYvnHUaxqOi487Lj5uBLWoT/wjfbl4MgMHF4OFL8kbEjm0Zjd/ffwLLe/qgle4P6XFJdX63dzMbzujA+5BPnwxfCrJGxMZ8dkzhLRpSOIfW9Db2tD23l4U5RVy4fi5K8a53xsPENgsAnujA80GxhMWF83JvSk4moyc3JtKq7u6cO5wGn998itt7+mJyd+Dv+cuq3Hd6W31DJ7+GBeOn8Mnsh4PzB9PWFw0Hw6fwqGNiTz42bOEt2nI39+vtqg/e4MDF1W9gpx8vMxGupv1bYtnsA8Pf/E8ZWUSHRAeF80PL3xaYV/dqKWdwZ4sM12PEF8Kc6rOVIPcC9n4tY7E3mTAwc2J0mLLWWi2Bntyz2QCUJyTj8nsnDH3dGff1ysqjpnnyVHzFF3Kx9WKn7aOljYn1ify45Cp5J27SKcp9xDSpSmhXZriFuaH0ccVWSZpHx/LVjWW1xqDPi8PxyvMH//oYB6e/yrhcdHY2Nuyd+lmjm47SIeH+hLVrTkHVlQeabc10yqvd/O0FoM74h3hD0LwzJr3cQvwJPNkBskb9tH1qUHkZ+ZYtK9yH6/l/uDg5IhboDeOJiNnD6dx9vBJdi/ZRNzwbtx2T082zv3zqjEwbwflx+Mf6E3K5gNkHE6jSUJ77IwOHFRjYGuwJ/tspkXZq6Ylb0jki6Gvk5N+kbHL38I1UNmTIPbO2zjy117qNQ23iOm16HZ+KoHILs0Y89sb5GVeIjQumrn3TiP+kTsqbKtrBzWJcTnC3vjv6DloaGjc9NR2B98GyJFS9hdCdAdSpJTJamc5TwjRH3hatTOiTEnJQ5njHlQuIoTQAclSykoj8+rDQTDKfHpzHpdSrq+Bf8XAXCnlCiHEkwBSyrNCiOHA+ZqUQfWjRuWoKVJKqa4zuIAyMt8B5a1EAdWP4ufMmzfvt3nz5k0AEpa/teCn5CoGhXmF2KjTNuwMDgihs5pmPs+969OD8Y8NrbDZ8eM62t7Tg4Zdm7FXHd0qpyivsGJaSLlWUV4h2+av5sTOI0T3bIVXuD8+DQP56dmPKczJxy86mCZ33kbSn9vQ29kw8J2HWfTSbMpKlM6dEILQttEsf1vp8HuG+RPUoj4FWblkn7nAyvd+5PCa3fSeOIo9v2wkTT2Pe4ivRbnOJB0n90I2Ng52ZCSfwj8mlPOpZ6qNy9b5qyv0vML9cfZxpShfmTaRcz4LFx834h/qy7qPf+Fs0gmie7ai46P9qo2DrYMdi1/+vEJv7UeL6Tf1fjo+2o+590zDPyYUF193Vs34iZz0LHYtXE/354bw99xlV9Q0T+sw5k6WvvYV+37bzAPzx/P5sNfp+do9FKp+XzqfjWuV6QmX20dBhZ69wR5htrjvxwlzyLuYy+lDJ9i5ZBMOQscrmz/k7KETqr0DVtbGqO3CXLd6u4Nr9yDPZhJ7T3dSl+1A6CztinMLsXFQNpayNThcthGCgLZRbJ72/ZXzGK2fvyjP0iYj6TilRSUAZCafwjXEh1UvfQFAwvyX+eW+d2ny4qAalc1aDH5Wr7OH57/KJ8OmcOfEUQTEhLLwxdl4RwRw9tAJwuKiLTr4V2sL2xaswSvcnzUfLSa4ZSSD33uEI3/tpSivEK+IANL2pFi0r5romt8fHE1G7vv6JWb1fom2D96OT4NANn+3CjsHO04fOEZ422iLDv7V2kH58YBGIRxctwcnoyOJSzcT3j6mooNvrmFnsEcIYTXtrFnd5Z7Pwj3YmyYJ7clIOUVhdt4V66cmur++/AXn7umB3taG3AvZCL2e/KzcSnGy3g5qFuNyynLPozO4IewMyKJcq5oaGhpXpoa729zy1PY2meZbPE4C9Opi0lKUUfdFwEbgNWAB8KuU8lfgdsBbXeQKyq41q6vujAOsROngH62JM0KIIUIId/WrDYCUsnxk3tYs7aCU8mJNyqDa17Qc5X7YCCEeKH+oMEv3FVX+MkgpS1Dm9JevWXixmuJV2lYpM81yNtOpvakEt1ReHftGB5GZlm41Lf6RO2iaEA8oi1vPHUqj8+MDaJoQj190EKXFpeRb+SN50kzLT9WylhbSuiHRPVuis9ER3DKSrNPK6FXC2w+z48d1nNqbWqEZ3Dqy0uv0jJRTbPriDz4f9jor3vmBrJMZVst25sBxi3INeu9Rcs9nE9IqsmKOfXUxsJYW0Dic2D5tEDpBeLsYTu8/hq2jHb6RyjNcUIv6IGWN46C31RPcqiFHtyRx8WQGftFBnD96Bnd1tDGgcRgX1fLVVDO8XQxtRnXngfnj8YsOZsCbowluHEazPnEInSCyXQxp+49abUAn9qYQ1rKhcu7oYC6kpdP1kTtppbYFRxcD+dlKJyO0dUNOJx0npOL8wVhrcwBpe1NraJeCo6cJgKJLeVyyYpe+N7ViWo5HdFCFjX+bSM7urPpIezlP+bQcr+ggsq3onrNi02P6GDyjgpT67tmSjAPHAXCu50nRpTxK8guvoWxXt0vbm4qU4B7ojV90EHaO9mSq9W/OyUpal9tC1TSA0/uPotPpKtqR0AmQynCwefsq161pux324Tj+fGs+F09mkLY3FRs7GzwCvfGLDsbGzqaSbk1jUH4849hZgpvVJzMtA//Y0EpaynXZAADf6GAy0zKspg18bww+at0ZPVywsbOlfscmhMZF03hAO3yjg7n782dvSDeqZ0vOHDhG/Y5NMLo7M3j6Y/hFB9PzhWHVtoOaxtj8HozQVbvAVkNDQ6Om1PYIfgrwphDic2AHyob9W4AvgBNCiN4oI96FKKPpXkKI+1E61U8DXwkhBkgpd1LNT/UKIR4DzFdiSZSR86p2ApgC7BNCZKHsgNNGCJEJfIwyb77MzN5JnQ9/xTKotjUqB8r0oSCUOflLgfdQ1gHYASUouwp9hDL/XqhudwdGAk2klEVmby2qzl9YBPyFssj39nOH0+j2zGBWvPtDhcGBZdt48IcJuPi4Ub9TEz4ZMAEkFmlC6Bj24RO0HNqJs4fSWDNzIQ/9NIluTw/G4OHMnsUbyTp13qr+6B8m4OzjRoNOTfhY1a+aJqVk4DsPk/D2w1xMy2Dlez/QcngXYvrE4eLjRrOB8ez/cxub5vxB/Q5NOLo5qeIc275bTcLbD9N8UAcKcvL5YdyHVsv25Yj/cN83LxPTuw32zo58MmACZ5KOM3Tm47gH+3Am6TgBjcNYMvFLqzGwlnbh+FkemP+q+odXsO7jXwlsGsHAtx+h/38e4OyhNL59+H0KcwtqFIcWQzvj4uNGk37taNC5KUII/tvvVRLeepgOY+4k+2wm342Zfk2x3fPLxopYPTB/PAtf/AyPpqE8Pn8irRI6AIKVH/+CT0QALfq147d3L49471m2jXE/TMLk40ZUp6a8P2A8Qui478NxxA3twplDJ0hap+zy2qBDY3b89BcdH7kDFx83Ijs15cMBr+IdEUCzfu3400w3cdk2xvwwsZKdNRKXbaP/+g9I+2sfEX3bsOyxD2n93CC2vP1jhU3Kn9sZ8NN4DD5uBHdqzE/9JgEQ2LExp83aiTkpf26n/0/jMfq4EdKpMb+P/ZC2zw5i0zuVdQf9eNlmQf9JZBw4Qa+Zj4IQpCzfwYn1iQAEd4jl5OaD1ZbtemOQuGwbncfcyZ1T7sXJ08SJnUc4tv0Q3Z8ZzHKz62z/sm08rLbNBp2a8JHaFizSgA4P92X1rEW0vacHRncX3Op5UVxQzJhfpuDs5cbH/S/7UdM21mJoZwJiQ+k0tj+dxvZn649r8Qjypv39t2Pyc+fMwRP89ua39HxmyDXFoPz48Z1HiO7eAt/6ATg4G/jw9pcqbJKWbeeBH17FWb0uPx0wESQWaWeTjjNoxmMIITi0ahcRHWJJ2ZBIcX4hPzzxIXd/9jTfPPDODekmrdhOyoZEUjYkYuPkwOgfJqC3s6GsrIyDq3fiFRFAk363Xdc9Uggdd334BHqTH7KkCFlsOaWtKl/OeuuqNhoaGv+7CClrZ7KfulD1AyAZ+EpK+YuabkTZArIrMBxlX/uFKCPTf6F0fIdLKdOFEKOAEinlt1b0/YDFKA8lA6SUx9T0wcC7wLkqWVyAfVLKBLWzPQylk/8Q0BGlg22P0oH3VjVW1aAMdwIGwLUm5RBCPAN8LaU8p2q9hjLPXqA8TNwnpSxVpwmZVN8mSSmTVPtXUNYO9JBSXl6JquCGslvPuvEhw09bqxcHFyMR8TEc3ZJETnpWtWnV5Uu9gs2N6Fuf2HBtVD1PdeetqY/VpYVfpSzXGoeaxOdaNc3JoQxHFyOR8bEkbznApSuco6Z29mqNOboYqR8fS8qWA1f0vaZ2kU7O1IuP5dTmJPKrsbM3Ga5qUxWdq4Gg+FhObk4i7wq6V7Opykldaa3GoDz+NbnO6le5Hq2lmds3qKM2VoystRiYHy9Jt9yGV/HHQHh8LMcq+WiZdrU8taFbTimyTq7jSduqbo9dO9h6htWJroaGSm38Sa81ng4Z9o+tYnnv6Pybquzm1FoH/59ACGGQUlrOFalZXnsppcU+dOr0Gynlv/ud6PiQ4XVSkXXVOm7aK8IK/54rRCGnDmYg2tdRjYWU6utEt7iOGthJnbWfo7gx9HUU21r/mXKV4jq6IhzqzOPap7SOYqB18DX+pdxUf9K1Dr5CbU/RqVOut3Ov5rW6ybS65aWGhoaGhoaGhsa/nH/boFxd8e8ZMtHQ0NDQ0NDQ0NDQuCr/qhF8DQ0NDQ0NDQ0Njer4V8+3rkW0EXwNDQ0NDQ0NDQ2NWwhtBF9DQ0NDQ0NDQ+OWQGqz8AGtg3/LoO12U3fUVQzqqs6c6uDFXF3F4Iy+bqJQVkfRtdWuCJoV1c2L30S72q8zt7K68TVTVzft67WWVX+kvXaYWAe782g782ho3NxoHXwNDQ0NDQ0NDY1bAm0OvoI2B19DQ0NDQ0NDQ0PjFkIbwdfQ0NDQ0NDQ0LglqKspmv82tBF8DQ0NDQ0NDQ0NjVsIbQRfQ0NDQ0NDQ0PjlkAbv1eo8Qi+ECJQCKETQngIIfyFEL5CCOP1nFTVsr2evDXUF1fSF0K0FUI4CSF6XcG/WinrtSCE6GfNbyFEEyGEd12fX0NDQ0NDQ0ND499PjTr4QggX4BcgEogBngGaAK8LIZyFEHZCCBchRE8hxBtCCJMQwiCEGCeEaGZF8lvA5Srn/FsIsUEIsabKZ5MQItbMLkAIsUj99yohhAAigM/NbGKEEA+p/9YBXwL2QDchRP9rKatqU+PyCiH2VPm+v5ryegGvAE+o5fhaCNFFPTwMCL1SvDT+XTiajIS3j8Hg5lwn+raO9oTd1ggXX/c60deofZy9XAlvF4Od0eH/2xWN/1EyLmQyasyz/99uaGjcEGXIf+xzM3PVKTpCCB+UDvkKlN2HAoBBgBtgC0wEZqN0Qn2BcOApYI2atkcI0R44JaVMUWU9gQVKX7wSjkBnKWWRlDKuGn++ROmcI4TQAyVAoTrynSellEKIYirvlHQAmCKEuAgUq+f/RtVpLIR4BhgLnKlBWZ8Fwq6hvPlVipBj5vv9wGwppQQeAr6RUs4QQvSRUo4UQoQKIb4DDgKl1uKh8vlDP03i0OpdrJm1qFqjAdNG4xURUMnOPM0t0AuviACObjlAcItIPhsyuVJ+7wb1uPerF7l4MqNajaulNejcjOQN+/jxqY9wcDEyeMZj2BsdOHc4jV9e+aJGWvbOjgyd+Tg6vR6PUF8unbvIoVU72Tp/FXd99CSzq/h9rT5Gdm1OSWEx275bRd7FHGL7Kk3RwcVA2q5kFr+sPDsOnvEYUd1bsO6jX64pFoNnjKVh12ac2HkEJy8Tnw+fSt6FSwDcOeU+Dq3ZTdLKHTXS0ul1PLNuOhdOnMMzzI/cjGwOLNtG2G2NOLxuD7e/cjc/PPkh5w6fvKZY9H9zNP6Ngsm/mEtRXgELxn5AabHSBAe9PwbP8ABKCopI253MH1O/qbbNWePJVe/g7O1G2q4jzB3xJjq9jifXvU/miXMALJ34FW3v74VXhD+HV+9mrepjv2mjK6VVzZd/MRdnb1fsDA7kXbxE3sUcFoydSVnJ5UvnWttXednvnPoATfq1I/tcJhdPZrBk4lzOHjxx3e3M6OnC8I+eJCP1NF4RAZzal4pvwyBSNibS66W7+HjAhIp4X4vfw6pcF8kbEvGPCam4xha/8sU1+2uuW5RXwN6HZqI32HP7yjexc3Yk68hplveZYLWuHTxdaPfZOFYOmAKAIcCDuBmPQJnE0deNwos5nF65m8QZi6ptL9dy7ZqnObgYGf3Hm9g7G7iQfIpv75yIvclA7xmPYufkyPlDaax4eQ493noQjwh/UlbvZvPMxRXnNXi6MPCr5zm77yge9QNwr+9PeuJxABZN+rJS/V+vjwPfG0PDrs3Jy7zE2YMnKq4zo6dLpXvZ9epXIPTYmHwpuXiy2rSs7Eu88vq75BcUVFsXGhoa/x5qMoLfCvgOpcPbHcgEPgXOSilHAvOBdKAD0AjwB9oDzsBxlE7yaDUv6gh7hpSym5VPOyllkWpnEkLYX8W324F5QDzwUtWDQoi+QoheUspSYCRwFpgM/Cql7KV+f0hKGS+l3F2TsgohXIHDNS0vltPByoQQUcAy1W+jEMID5U1BlhAiCGgghJgLBAOFV4lBAqD/dOAkXHzc8AjxtWoU3bMVOr0OczvztOCWkTiajHx139s0ubMdjq6WM5IGv/8oQrV3NtOoaZqzjxvHdxyiMCcfjxBfmia0Z/fC9cweMhl7oyPtH+pTI60m/dqxYfZvbJm3gpLCYtZ9tBjXQC+GfTQOO4Nlk7kWH90CvTm0ZhcHV+3EKyKA5PX7+HzY63w+7HWObjnI1m9XVWgGNo0g6/SFa4pFm5HdMbg5MWfkfzh/9AwndiXjH6O8nAluFYmTl4mklTuuWl/lab4Ng9jz60Y2zfmTI+v2MKv3S3hG+LPz579YM2sR2xesIbhVZI3bgYuPG43vaEu92FD+nDafOSP/w6X0LOp3bFLhY2hcNEsmfsnsIZMx+bkTGhd1lSZ6mR4v3oXQ6ZgS8wBOXq6EtWuET8Mg9v66iTnDpjJn2FQ8QnzQ6XXMHvgazj6uuIf4ENWzpUWaeb6/5/xJYU4+6z76haL8Qha9OJv0I6eI6t7imttBefv6Ui17l6cGYXB3ZuOc3zm8ZjeLXpxttXNf0zpzcDEy6N0xuPi6VRzzDPNj1YyfWDXjZy4cP4dboPc16zbt1471s39js3pdrP1oMaFxDTmyfi+fDZmMndGRgNjQ69Ytbwt+nZvQbMLd5B4/x4+Ro7EzGajXq6VFPGxNBuJmPIKN2TUZMaIL216cw6E5y7AxOrDthS9w9HHFKdTHanu5lmu3alqvV4aTdeIcsxqNxsHFQESvFkQntOfAwg0sGDQFO6MDLR9W7jnfJUzGyccN15DLfnQcPxxHDxeEXsfKV+eSdTydZS/M5vuhUyvV//X62GZkdzxCfJn/2AyO/LWXooIi6ndsgoOLkYHvjqm4l91IDMrRG92p+nN1VdP0eh3vTH4Jo9FgtS40NDT+XVx1BF9KuUQI0QoIlFLOUpN/F0JMEUIsRengdgVmcLkzaofSWX4TZeTbFfhNPeaL8kBwNd4GFgNLrRwTZr79DcySUk4WQqwUQqwDPgJaqOe9X7XNUUf2DwPdhRB/oEy9+UoIUSyl7FrTskopS4UQNS2voxBiPcrbiTmAH/AO8JKUcguAEGKsmf2DKJ39WK7yACaEeGjOnDlTfv/99+xLl1JptDGR4FaRnD96xsI2NC6KvUs2A5Cs2vlFB1ekSVnGxZMZyLIylr/7PR0f61cpf/PBHSkqKCL3fDYAKWYa+1SNK6X5x4TiExnIvqWbKS0uIbhVJPmZOXiG++HgYsDk505xQWGNtLbMWwFAn4mjyL2QTc75bI7+fYBT+1KJ7WP54ic0LqpGun7RwRTmFrBvyWZsHe0IbRtdEU9nHzecPE2c2pcKQJMB7UhPOYWdo/01xaLV3V3ZOPt30nYeIaJ9LE36t+P31+ehs9Ez4M3RHFy9i6juLa5aX+Vptva2RPdoRdMB8aSnnEan17Fv6WbsnRzxaxRMdM+WLHzhsxq3g5S/D9B34kh2/7IJO0elg2F0dybnfDY6Gz393xyN3s4WFx83AHIysrF3NmDrYMfA98bg5OHCmYMnWDLhS6tttn6Hxuz6eT0Ah9fuJvbO2zi1N5WoHi0JatGAiyczKMjKZd+Sv9WY7Vd9DLFIs7G3q8hncHdm2Rvfkn8xl+L8QsLaNcK7QT32Lf37mttBefsqL7vB1YmsU+eJ7tGKsrIy/BqF8Nng1ygrrfxzKjWts8TftzB/7AeMW/ZWxbGt367CrZ43kZ3tcDQZK13DNdXdXOW6yD2fzam9RwmLi2LHD2sx+blz8dT569Ytj0dWRjYGP3fyTl/A1sWALCnDEOBhUdeyVLLhkZnEz3m6Im3PtB8AiLinGyV5hRReuMTZDfvxah0JC63ft2p67VZNM/l6kHP6AvYuBspKy3D28yA/Mwf3cD/sXQw4+3tQXFDEITXf8Y2JBLSK5OLRswTeFk1xnnJ7P7RkM37NIjB4mUiY+yxndqXw3bP/raj/6/Wx1d1dWfPBQpLX78PB2UD8mDvIOZ+NLCtjwdgPGPHZMzccg/NHzxDWNhopJcjLb4SErYNFmpOxzpeZaWj8I2g/dKVQ0110zgDthBArqqSbgJFSygvqaHv53cJRSjlBCNEN6CGlfL5KHhcrWraAjZSynfq9CGXe+3NV7C4BWWbf7YFoIcTDKNNhugMhQAYwSEpZAqCOmgcDLwMvSinvFULMV/999FrKanbempQ3X0rZvvyLEGKElLJPFe1pwEAgCOiiardBeSCoyico6wOUGzR433vvvT1eCRm+SxkZtz4SZmewJ/us4nq5nXma0OkQQlCYk09OehZ628tNw9HViaYD2pORchrfyHrValwpzTcqiPSUU/z1ya/cPn4EXhEBLJ00lwZdmtL23p6kJ59GZ6u/olbzwR3xivCn2cB4AELjojmx8whpO4/g4OSIv6/bNZe93xsPENgsAjujA7YOdjh7u7HyvR9w8jRha2+Lk6eyVCRuVA+2fKM0Cb2tnqDm9fnu0Rl0f3bIFcsd0SGWoBYNiH+kL44mI75RwRVTWgIah6HT6ygrLaNZQjznDqfx1ye/0vaentRrEsb279dUG4sWaizys/LIy7yEZ5gfR7ccoEHnphX2Dbs2x9ZgT3FhUY3bQWCzcPIycyr8CGkThaPJSNrOI7QY0olzh9M4n3qGdg/2JiI+lrDbYlj+1nxaDu/CuUNpzJ/+E3d9/CQ+DQOJG9UDzzC/inOnbEzE1sGWc0eUKQF5mTn4NAhk6zcr+WLo6+SkX6TP5HvxCPMje/7qSj7aGuzJPptZKS15Q2JFvrHL38I10JtjW/9CSoju0ZK8zBwuHD9X47KXp/V74wE8w/ywd3LELdAbR5OR75+YxZqZi/BpGEjXpwbSoHNTklbsuKZrrEWV9mvy86hk7xMZSEibhuRdzK303u9adcPU6+LEziN4hPgSGteQtvf2JCP5NPlZudccj3ICm9dXHj52HKEoKw9bFyMNHuhJzol0bKysGyjJuTw7sdW0+3EOv9wWfG6LJu3PbeSfvUhxTn61I/g19dFaWkF2LkYXI83u60HWiXTsjA4cWbadsC5NaXZfDy4kn0JnoyfnjNKuii7l4xbii85WT9txA1g8+n3Ce7Qg50wmeecvsXbyN5gCvXD2c69U/zX150r3AhdfdxxdlOusNmOgt9XTeVwCZXkX0LtcjrHO4EZp9tlKaRoaGrcWNe3g2wA5Usr+QojuQIqUMlntBOepC1WfVu2MKFNN8lDmrgeVi6gLXJOllN3MxdWHg2BgfJXzPi6lXF+dU0KIQSij3RnAXOAOKWWZEKIMOFbeuVe5D2VuPUAXdWFuS2C2OoJ/e03Kqp63RuWtKeq6AYALQCeU6T/dgAIsR/EfrvJ9BsrbAewMDightqQorxBbBzvM7czTykrK0NvaXj5u9uq25wvDWPbWfBr3a4fQ6SppFOYVYlNF11qavZMjiT9uISc9ixM7DtPq7q50f24ov7zyBYU5+dz2QG8iuzS9otb2BWvwCvdn7UeLcTQZGbfyHdb995dK9ta4ko+LX/6c6J6t8Ar3x+hlwtnLhI2DHXYGB2zsbBFCefAJbRvN8rcXANBhzJ2cSjxaMYJ3pXIfXreXPb9sJG3nEaJ7tqL96D4VNjt+XEfbe3rQsGsz/BuFsPW7VeSkZ7Fr4Xpi+8Zdsb62qbFYP3sppUUl9Jk4itKSUjxCfMk8kY4QOlZ/sJDsM5k0G9iBvz7+tUbtwCs8gLQ9KeSkZ3FgxXbunfsis3ors9/8GoWw7btVHF67hyb9bqPny3ez5evlFOUV4hnmT1CL+oTGReHgYsDF171irYI5Ub1aYe/sCICDyYjQCc4mHae0SLlUM5JP4dMw0MxHe4QQFOUVWKSZ58s9n4V7sDftHurDoVU7sLG3Iz35FB0f7VdRbzVtq4tf/hxHk5F7v36JD3u/xG0P9ibr9Hly0i8S2Lw+uRcuWZ0Kd7XYbjNrvwDPb5pZyb60qISfnvmYQe+NIaBJGGm7kq9Z19Fk5MmV77BWvS5aDuvMgeXbWf3BQto90JsWgzuy9btVNdI1v6YcTUbumHQP346ZTj3ArVEQW1/6knMbEmnz/kO4N7nyHgBbX7g8998Y5EX3Xydx6PNlANio9WmNmtaZtTTf6GBWvzKXExsT6fnOQ/g0DsOjfgArXp5DUU4+LR68ndCuTbFxUO57tkYHEILWj97Brq+WU5idhywtw8bBlnOJx3D2dwchuJB8qlL919Sf6u4FjiYjcff0YP+fW2s9Bh3G3MnfXy1n0OQ7KvR0jq6U5WeD1MY5NW5N5E2++PWfoqbbZJpv3TgJ0KuLREtRRt0XARuB14AFKHPcf0WZI++tLtQFZUrM6qo74wArUTr4R2vijBBiiBDCHWX6zp3AOSlltSuDVNseXO7gr5JS9gfWAw+ade6vWlaAayhv+flthBAPCCGerJLuK6r0StWHkvZcXtvw4hWDAdtVe/yig8hMsz776eTeVIJbKnOxy+3M08pKSio6Xn7RQZVGfUPaRNHzxbuoHx+LV4Q/3Z4ZjK+qccpM40ppZw+dIKRVQwDCbmvExZPnsXW0wycyEKETBDYL5+LJjBpp6W31DPtwHDt+XIt7sE+lY9aoqY+n9qZSViYJbhmJb3QQNg52ZKalE9w6krRdl0fWwtvF4B7ozeDpj+EbHUynx/vXOBanDxyj8+MDaJoQj190EKXFpeRn53H+2Bnc1XnXAY3DOH/s7BXrqzxt8HuP4hsVxKl9qUR1b8GZA8dp0u82/BoFA8rC4ILsy6O2V2sHQidASvS2ehKmPUzyxkQunswA4PyxMxVzw20c7NDZ6NgwW5lZlpFyik1f/MHnw15nxTs/kKXmqUrarmSiuyvztQObhZOReoaB743BJyoIoRNE9WzJie2HCG7ZQI1ZMJlpGWocK6eZ5zN6uGBjZ4utoz3BrRqSmZZOUIv65W+5rqkdlLevZW/N5+JJ5dx9X7sX36gg/BoF4x7kw5kDxy3KdrXYVr0+C3MLKo51fKwfNvbKrcfRxUhBdt4165pfFx7qdeHk4YIsU+q1XrPwSvG4Vt0/1XgAFOcWENi7JUIncG8aTtGFHKv1XRVbk4HbPhrLodnLcItR2qhro2By06y3l2u5dqumFeUVUP/2FgidwLdJGPmZl7BxtMezYT2ETuDXLJzstHQC1DUq3tFBZKelE9w+hqajujNkwSvYm4x0njSK26ePIaRDLJdOZhDRs2Wl+r9eH08fOEZomyiGfTiO4zsOczrxWK3HILxdDHGjuqM3+SH0duidPBF2jugcXSqlaWho3HrUdAQ/BXhTCPE5sANwArYAXwAnhBC9UUayC1F2qfESQtyP0ll+GmWe+wAp5U7UzmhVhBCPAeZ3OIkyIl7VTgBTgH1Syv3lW1eq2AkhPkCdzy6EaIiykDUd+E4d3RfA7eq8+DDgB3XE/ysp5YdXK6uqW6PyokwzCgK2ojyMvAfcJ4SwQ9n9ZzbKeoHfUNYVCPWtwUigiZSyyOztRnW76CwC/rp9/AgadGrCxwMm4BURQJN+t7Hi3R8qjA4s28boHybg7ONWYYekIs0rIoDSklLKdYryCuj2zGBWvvsD07soc0HtnRx5fvOH2DraU79TEz5RNR78YQIuPm5XTEv5ez/jVrzNc5tmYnBz5usH36HJnbfR7z8P4hrgyYkdh1n62lfc89WLV9VqMbQz/rGh6O1t8IsOIbRtNO5B3nwyYAK3PdCLbs8Mtih7TXxEQvuH+xDRLgah15F3IZufn/+EXi/dXWnR8eyhU7B3cuTBHyZgY2eDjYMdB1fvrFEs5ox4g3vnvki3pwdj8HBmz+KNZJ06T3i7RrjV8yb2jrbobW346dmPGfbhE9XWV3namaTjDJ0xFqEXODg50qBzU3wi65GecoYxv0zB1sGej+54pcbtwK2eFyWFxTyx/G3c63lRUlzMs+tn8Oeb37F9wRoS3nqYxne0xS3ImzUzF1FcoDwIbvtuNQlvP0zzQR0oyMnnh3EfWm2sq2f8zNNr32fMr1PxiazHire+p+nAeAa+NwYhBEkrtrP+4yU88MOrOKsx+3TARJBYpJ1NOs6gGY8hhODQql1EdIjl5J5U6ndqQv2OjcnLyuXjfq9eczsob1+dxvan09j+bP9xHTb2ttw372XsnRzZNPdPss9mWm1nV4ptRZpKbuYlmia0x9nHDUcXA+HxsUR2bkpRXgEZKaevWbfF0M4EqNeFv3pd2NjbEXtHHPEP9yUnI4uFz3963brl8Tj95Up2Tv6Wzt88T/iILsjiErY88xmxzw9m71uX42GN6LF3YgzwwK9LY9xiQnBvGo5rVCDL+06s9r5V02u3alr2uYvcO/d5Gg/vQllxCcue/4zGw7vQ/c0HcAnw5PSOw6ye9DUJXz+P0ceN0E6N+bb/JJIWb6o4/9AfX8XOyYGSgiIaDe5AYLtG5GVkkbxh3w37OGfEGzyycApGd2fKyspwC/TCydsVg8lYazHY88tGACZuex29yY/SnMoPUtbSvpz11hXrUEPjZkd7N6UgzEd0rBoIEQ98ACSjdIJ/UdONKFs7dgWGo+xrvxBlxPkvlA7tcClluhBiFFAipfzWir4fymJaG2CAlPKYmj4YeBc4VyWLC0rnPkHtrG8Ctkgpn1AX2I4HdqGMbOcDr0kpfzI7XwzwrJTy3uss652AAWUh7VXLq27B+bWU8pyq9RrKPHuB8jBxn7podzjKPP+OwCQpZZJq/wpwB8rc/mwrVQTg9t1jMy4c3ZJETnpWNSbg4GIkIj4GczvztJLCEovjVV+cX03jWtOu1ceq+Wqiea0+RvVogd5WT9KKHbWmaZ5W/j21lnyuq1iYY30CxfXhGuBF2/t7suOHtaQnpVXjo9LhPVbJR8u06vJkp1+sRvfG2uW/pc7qsi3EFCmtwdZkwLdDLOl/J1FwhXNUR9X8iXbW/xbdyP3F19mJ4PhY0jYnkVeNj/YmwxVtrB3P1FXuQtzovaA271/W9CZue71a7evF1jOs1jU1/rXU5p+IG+b+kEH/2BydL47+eFOV3ZyrdvD/ESeEMEgp865uaTVvY5QOf5kQwqbKvPv/GV4JGV4nFXnTttxbgP//K6/m1FU70NWRcl39AMm/qc7qivIOfm1TXQf/RnArq/GPtV8TVTv4NztaB1+jjrmpugr3hQz8x27Vc47+dFOV3ZyaTtGpU663c6/m3WP27//Jzr2GhoaGhoaGhoZGOTdFB19DQ0NDQ0NDQ0PjRvl3vV+rO+rm/aWGhoaGhoaGhoaGxv8L2gi+hoaGhoaGhobGLUHZTbC29GZA6+DfItTVKo+6WqRWFw3vTB0tfNPXiWrdxfZ8HcShuI6Wl5bW0cvUm3bV0z9IXS1g/tuuburMtg78Pa2rbnfhG6Ou7gl1xWstq/6G5I1TFwt3QVu8q6FRW2gdfA0NDQ0NDQ0NjVsCbfxeQZuDr6GhoaGhoaGhoXELoY3ga2hoaGhoaGho3BLU1e+g/NvQRvA1NDQ0NDQ0NDQ0biG0Dr6GhoaGhoaGhobGLYQ2RUdDQ0NDQ0NDQ+OWQGpTdABtBP+GEEK0E0KEq/8eLYRwv4a8V9wTTgjxj+7E5mgyEt4+BoObc6W04PgYHN2c/klXNDRqjMFkpEH7WIxm7Vaj7nA0GYm8wXgbakFDQ0NDQ+PKaCP4VhBC6KWUFhsoCyF0gE5KWaImvQC8J4QoAB4D5lSj9z7wNtAWCAE+AX4VQvSWUuarNj8C8cBWoA3wsRDiCPAn8DVwu9l5y/kciAJ+q64sA6aNxisigEOrd7Fm1iKraU5ergz/5CkOrtzB7eNH8MXwqej0eoZ/8hR2Nno8IgLY8cWfrH/re6vn6PHWg3hE+JOyejebZy6uSDd4ujDwq+f5uvd4hF7Hg+vfI+t4Om7hfuSnZ3H4ty1sNbM3p9tbD+IW4c/R1bvZOnMxQq/jPjU/wJoJc3EN8aXp/T0BcK8fgLDV89dnS1mtltMaCdNG4x0RwMHVuyrsqqY5uBgZNuMx7IwOnDucho2NHr/oEGyN9uRmZJO2O5k/pn5zzXG2d3Zk6MzH0en1FOUVUJaVh1uYH6mrd/N3NXHo+daDuEf4W9iYx7b8+50fj2P+oCnVlvN641FhO+V+ktbsYv/KHQye9hA+EQEcWL2LlbMWVqtf1c7B2ZERM59Ap9dTmFfA3LHTGfT6g6rNTpZfQWvotIcr2bl4uXLfJ0+TuHIH/caP5KPhUygtLmHUzHHo9Dq8Qv3IPpfJ/lU7WXEVXW9Vd4Xq40hVoyivgK/GzqC0WLkdDJxyP54hvtgZHCrsa0P37hljienekpUfLa41TSdPE88tf5v0lNM3XE/zxs7A4OrMPZ88xb6VO7hn1jgyjp1h3/LtLLuC7l1qne1fvZNlap3dr9ZZ//EjmTV8CrkXLgFK+/IM8VFje+P+lhaX4uRp4unf3+TC8XO1qnnPR0+SkXoa74gAklbvYtUVdAdNe8jCzsnTxIiPnuTjIa8B4OrvwZD3HkWWSVx83Mi/mHPDuubHDy7bRu/xI/hy1JuVNGpy3wIwerpw10dPMnvIZAC6PDUQvckPACH0lBXmUJZ/sVpfMy5k8vQrU/nqv+9Ua6OhUVvUzS91/PvQRvCtM1sIsVYIsUYIkSqESBJCrAHWAW8BCCF8AH8p5RpgJGAC/hBCrBBCrBRCJKh2NsD3wOtAqfp5EZgKFJWP5EspBwFbpJR9gd1SyleBVoADUGilc5+A8nsrtwH+HiG+FoWI7tkKodfx6cBJOPu44RHiazXNp0E9fpvyNWs/XMyRdXvwjwnFp0E9DizfRsbBNHZ/vQLPhvVwDfGxOEdEr5YIvY7vEibj5ONWyabj+OHYONgB4BUVRNIvm9gx50+Ort3Dt7e/YmFfTriq+YOZpmdUEAd/2cRPQ6fy09CpnD+YRvKf2/hp6FR2zfmTSycz+On5T3FRy2SNRj1bodPr+HjgpAo7a2nNE9qzc+F6Ph0yGe+IAIyeJs4fO8u5g2ksfP5TTH7uhMZFXXOcm/Rrx4bZv/HlyP9g42CHc4Cn1biVU7+GsbU3Gbj9vUewdbSvtpw3Eg+A0FaROHuZ2L9yBzGqzayBEzH5uOFZjb41u+b92rN29m98OvINLqVfpNdTg9HpdXwwcAIuV9CKVbXM7Xwb1GPRlK9Y8eEiDq7bQ72YUFr0a8+a2UvZOG85xYXFrPxo8RV9jFXraebACVV8XMonI98gOz2Lhh2bqjFoSEBMKFlnMyvZ36hubM9WBDetz8XT52tNE2DkrCdqrZ4iOzbFt0E9fpnyNWePnCT7bCa/vfs9Jh83vKrRbaz6O13110uts4VTvmL5h4tIWreHwJhQAMJaNSQgJoSss5m15q+ji5EH576I3kZfq5rD3h2Di68bQq/jo4ETr3qNVbVzdDEy5N0x2BnsK+zaDO/Kolc+Z+PcP7E3OvDzK5/fkG7V4/1evx+9XeXxvJretxxcjAys4u+q93+iNOs0pVmnkaVFlBVesuonQFb2JV55/V3yCwqqtdHQ0Kh9tA6+FaSU90kpO0opO6GMkr8ppewkpWwvpXxaNRsLXBRCOAP9gbnA61LKbkA3YLE6ZWcFMB5l5H4hMBroATwJ/A6ECSG6CSG+AJoIIWYDUUKIz67iZieUBweAVcGtIi0MQuOi2LdkMwApGxMJbhVpNS15wz7Sdh4hpHVD6jUJ58SOwyRv2IeLtxvnD53Et2k4B5dsJsDKOQLbRnFI1Tu+MbHCJvC2aIrzCslNzwLAr1kEET1b0nXKPbgEeCL0Ok5sTMTfima9tlEcVjXLbfyaRRDesyWDf3qVnjPGIPSXm25g+0YIvY7EP7eSvDGRECuaAGFxUexVdcvtrKXlZebgFe6Hg4sB92Af9izeiGeYL/t++5vgVpHkZGRj72y45jhvmbeC5PX7APAI9iFl5c6KuNWrJrYHzWJbr0ps89TYylLJr4/NpCgnv9py3kg8dDZ6Br85mgtp6TTq3oLwuGh2L/kbgCMb9xFajb41u43zlnN4/V4AnNxd8AjyYdeSTapNImHVaEXERVvYHdqwj2M7jxDWuiFBTcI5uuMwG+Yt59D6vUTERZN7IZuc81kc3phYrY8RcdHsVnXL7TaqGuU+5pzPQmejZ8ibo7GxsyHzZHol+xvRBWgxIJ5zKae4lF47vgJEtG2EwdWJi6fOV4q/NWpSTznnszi8YR/Hdx6hRf/2SClJ3XGYQzWss0NV6ixcrbPUHYfR2egZ9uZo9HY2XDyZUWv+lpWVcWLXEbLPZtaq5ryxM9Db2rBHtU++im5Vu7KyMr4dO4MC9XoF+POd7zmXfIrwuGiKcgvIvXDphnTNj0spuZSRZaFR0/uWLCtjwdgPKDTztxxhY48sK4Wy6n8xWK/X8c7klzAaDdXaaGjUJmXIf+xzM6NN0bkOhBC+wDDgGPAEytQcLzMTWyllEXAB6CSEiAeeAyYDWYCfaj9BSnlcCHEGyACCUUb3G6A8FEwG7LGOccSIEXHffPPN5AEDBrjc0bSLhYGdwZ7ssxcAKMzJxyPEx2paOTF94ygtKaWstKwiv1tsCKUlpRRm5+PZIMDiHLaO9uScUf6AFl3Kxy3EF52tnrbjBrB49Pv0++wpAM7sTuH7IVNp9+wgbA32hHRpStGlfFytjFBV1XQN8eXE+kR+HDKVvHMXGbH8TYb/PpWCzBxAeSDY9M6PVstUSddgT1aVsltL2/3LJiK7NOW2e3tSlFtAesppEn/bQsNuLSgtLiWoRQOWvzX/uuMc2Lw+to72HN+wXzlegziU2+hs9dw2bgCLRr9PfzW2HV+5C/cwP7yigxi64BUC20ax9fs1tRaPB+a9jMHNmeBm9Wl8e2vcArzY8v1qAApy8qsdZbQz0yq3G/jGA3iH+WPv5Ih7oDcGk5EVHy6ssPGsxlc7g0MlLXO7Zn3b4hHsw+gvnkeWKTfciLhoju48wrGdR2gQb7gu3eDm9QluXp8+LwzHxcsVg5sTTu4unNiTQvt7enIu5fR16d736TOEt4nG6O6MEBDeJpoPBk2k97NDrzsG5r4KAf5RwTi6GDmxJ7lS/K3rWtaTpe5dFWnhamxlaRkFOfl4VeOvvcGBi2a6XlXqzDPYh4e/eB4nDxNGNbZpe1Jod09P0lNOXZe/9376NGFqbMt9rYsYuPp5VFzfV9OtamfeUU544wE8w/wrvofHRZO4bCuXzmXekG75cYOrE8EtGrD/t81ExMdWq3Gl+5a1jn05OgcXSvMyqz0O4GQ0XvG4hoZG3aB18K+P+4BXgQeBd4ESlI75dCHERaAY6A4ghJgBHEd5IIgHmkspnxNCNAYeVfOBMp9/tpQyQwhhK6U8CzwshGgFXP4LcJmcefPm/TZv3rwJQMLytxb8lFzFoDCvsGIah53BASF0VtPKWTLhS7o+PZjIrs3Yt+RvCvMKOfjtGkK7NCMwriH5aofanOK8QmwcbAGwNTqAELR+9A52fbWcwuy8CruMpOOUFpVQnFdIYXYuriE+ZJ9Ix9pa46IqmkKIivwAe79Zic7Whp2f/Q7A/X/P4OSWg1bLVFXXtkrZraX1fG4oi175gsKcfCLaxRLTuzXL31pAx8f60aR/O3b9tI6ivMLrirOjyUjfSfdweO3uijLaqWW8UhzKbdo8egc7q8R2+UtfADB0wSssGDqVtq+NsCjTjcTj7METrPzvYg6u3Y13uD/3f/5cFRvr68WL8gos7H56+XMcTUYe+vpl3u/9IvEP9jazsb+CrwXV2v00YQ55F3M5fegEu5ZswmAy8uLKd1n130UA2F9Bt9BM19zOYDKSMOleZg6cQObJDBJeu4/9q3bQsFNTjm4/RNO+bck6c+G6dF193Xm39wtknsygxxMJFOYVIktLa83XHk8ksH7un4S1akhoq4aV4n/12F62czQZ6T/pXmYNnEimOrIOcMfEUdgbHWjUtTmlxSXX7C/Aj2Z1FtaqIYmrdhBjEdtr99fk68H7vV+s8LffxFF1EoPxm2bV6BoorEa3nJ9f/rzi3+6B3jz682tsnPvnDeuWH7/9hbvY/etGbHWWezZc698HC4QOdHooqzp7VEPj/xdtFx0FbYrO9fEmytQbpJQFZvPjn1Sn8pR37hsBsUAXlOk0U4EHhBBLgDeAVkKI3oAbsFVK+Z2q860QYpD6bxdgkhUftgPt1X83yUxLtzA4tTeV4JbKK17f6CAy09KtpsU/cgdNE+IBcHAxUJCdR/wjd2Bjb0tAq0gcXAwYPE1kWznH2b2pFdNyvKODyE5LJ7h9DE1HdWfIglfwig6ix7QHuX36GLyigji7L5Xw7i3IOHAcL9W+Kuf2plZM3Sm36TF9DJ5RQQidILxnSzIOHAfAuZ4neeez8WmszOX1U8tkjZN7UwlRy15uZy3N1tEO38hAhE6gt9PjEayMPNoZHbB1tGfD7MprmmsaZ72tnmEfjmPZW/NJ/ftARdy8ooPIukpsy22C2sfQbFR3hprFtiblvJF4nD92Bo9AbwDqNQ4j49hZQlUb/+jgavXT9qZa2Olt9Yz8cBy/vTWfzJMZnNibQmhLpQMWEB3MhWq0rNl1eeROWqrt1tHFQH52LnpbPfd8+CRbf1yHZ7BvDXy8rGvu46gPn2Sp6iNAxrEzuAd6k7Y3hZjuLck8mVEruvXbxeIR6M3d0x8nIDqY7o8n1Ipmu1E9aRDfGJ8Ifwa/OfqG6wmg8yN30CIhnhN7U/AO9SM/O/eqdRZWpc66PnInrarUWcaxM3gGepO2N5VGNYptzfwtt3VQp9PVliZAYW5BxXVyJV3z6+lKdo4uRu6a+TgbvvwD/+iQG9YtPx7aJoq4Ed1pMqAdvtHBdHtmcIVNTe9b1SHsDMiivGqPa2ho/P+idfCvAylldY+H09WFuRuEELFSykQpZRcpZR9gIHAJ2AP8R0rZV0rZVUr5m5QyDUgTQqwTQqxAeTPwuvrvV4FAK+dahLK49z1gyLnDaZVu3gAHlm2jaUJ7bh8/gpg+cRxcvdNq2tZvV9F0QHseXPAqOr2OI+v2sPXbVXiE+NL2qQQC2kTiExvC+UMnaffsoErnOPLndqIS2tHx1btp0KcNKat2sWDw63w/dCrfD51K+v7jLHthNpumL+T26Y/Q6pG+2Dk7EtK5KfX7tOH8oZO0raKZ8ud2Gia0I/7Vu6nfpw2pq3axZfpCek5/hOF/vMHpHUc4sT4RgOAOsRxZuoWGCe3oM34EsX3iSFq9E++IALpXicf+ZdtoltC+kp21tDUf/cKA/zzIxL2fU5xfhGe4P7ePH0HLYZ3Z9t0qOj7W77ri3GJoZ/xjQ+k0tj8t7+pMs3t70OnVu4lU42AtttEJ7SpsymO7YOhUFpjFtirWynQj8di6YA3hbaN5dMEEbhvZnUWvzaVFQjx3jB9Bkz5xHFi9E5+IAHo9M6SS/r5l2yzsWg/tTL3YULqN7c+Y+a8idDpaJsTTb/xImvZpy/7VO/CJCOD2Klp7l22zsNv07UpaDohn7IKJCL2Og+v20GZoF+rFhhLcogE9nxzEg188XyPdO810yzW6jR3Ao/Mn0LRvWzYvWE1E20bEDe9GTPeW2Dna1Yruh0NfY3r/8RTlFVCYq4y61obmR8Mm80H/VykrlRTk5N9wPTXpG8ff366ixYB42g7vhk94AA07NKFpn7Ykqv72rqK7Z9k2WiXE01/1N3H1DjZ+u5JWA+J5fMFEdHodSev28HdFbLuqsbWvFX/LbZ09TbXSVss1AXIzL9EsIZ6+40fQ2Owa61FFN3HZNgs7a3R69E5c/T1o0Kkp3Z4cxPBZ425It/x40qodlBQU8f0TsyjMzmPFuz9U2NT0vlUdOlsDZcXawlmNm4+yf/BzMyOq76v+byKEeBFl0Wz5qLwfUAScV7/bAT8AXwLzpZRd1XzjgfXqrjrmeg5AT+BxlBH8v4GPgYvAZ1LKfVZ86A/Uk1LOuoq7bihTgdaNDxl+2pqBg4uRiPgYjm5JIkddlGktrTp8nZ0Ijo8lbXNSxaLOqtibDFe1qWofFh/LyatoBl3Fpqq9S4dGpF6lTA4uRurHx1Sys5ZmjrEG8bqeOPs4OxESH8uJq8ThajZVOa8ru2qZzP2uSTyKq7zydHQx0iA+lpQtB7h0Bf2r2ZUicXQxEhkfS3INtGpiByBq0cfrtb8W3brQ1CHqRLe4jurMto78rQtNvWpfX7W/0jVWU7trtb+anfnxwvRsqxo38vdh4rbXr1qW68HWM6xOdDXqlCv+rs8/TULwnf9Yx/bnY7/cVGU3R+vgXydCCH9ggZQyXv0+CaWDv8LMxhNYACwDPpFSXjQ71h1lJ54nUabxPIMydx/AHWVxbXmnXQ/8LKWcWZ0/40OG10lFupXVzUueulj8cUZXN8/TdfWLY651FNvzdRCHqh382qK0jnRv2jvuP4iujqJQV23B9l9Ua//orxDWAnURW62Dr2HGTXXxDgi64x/r2C48/utNVXZztEW214mU8hTKotny75Os2GQAXavJvxxYrn5NBX6pfS81NDQ0NDQ0NDT+19A6+BoaGhoaGhoaGrcEN/v+9P8U2iJbDQ0NDQ0NDQ0NjVsIbQRfQ0NDQ0NDQ0PjluBm393mn0IbwdfQ0NDQ0NDQ0NC4hdBG8G8R6mrHDLs6msqWrq994brahcO2jmJQXEdr7yf8N+7qRtfIhcmLal0TYMZpnzrRHYHlry7XBgcKXWpd884F3WtdE6Dwv3PrRLeuhscS13rUjXAdkCvr5k/nMse6uSnUhbevtHylDlRh6rapdaKr7c6j8b+G1sHX0NDQ0NDQ0NC4JZDaIltAm6KjoaGhoaGhoaGhcUuhjeBraGhoaGhoaGjcEmjbZCpoI/gaGhoaGhoaGhoatxDaCL6GhoaGhoaGhsYtgZTaCD78i0bwhRCeVzj2iBDC0ez7k0KI+lbsAoUQOiGEhxDCXwjhK4Qw1pXPVs7vK4Ro90+dT0NDQ0NDQ0ND43+Pf00HH/hNCBFkniCEsBNC2KKUo5kQwqAeGgEcrWLrAvwCRAIxwDNAE+B1IYSzmZ6LEKKnEOINIYRJCGEQQowTQjSr6pAQ4g4hRFuz798KIezNvjsLISaZZRkFtLBWOCHEZCFEJyHEVCHEi2reP4UQ+pqF58ZwNBkJbx+Dwc3pnzhdnXArlOFWQbg4Y9+qBTpT7W8rqVH7CKMzNrEtEM61W191pauhkXEhk1Fjnv3/dkPjJqTsH/zczNzUU3SEEBuAS4A9Ssf8UyEq9gm2A+YCfVDi3AGwE0K8DgQBy4VivAT4CvgWWKHaBgCDADfAFpgIPAuEAcMAXyAceApYo6btEUK0B05JKVNUHzYBnwED1JH5TCllYbmDUspL6luDB6WUs4HhQIEQIkE1yZRSDhBCOAHZwG2AN+ADhAC5UspSIYRO1avanj4HooDfqothv2mj8Yrw5/Dq3aydtchqmpOXK3d98iQHV+6k1/gRzBk+lbKSUgbNeIyAmBCQsGfucrZ9sNjqObq8/SDuEf4cXb2bbR8sRuh13LPhPbKOpwOwbsJccs9cZMjSyTi4OZF97Bwf9p9AWUnpdflbTt8p93J4zW4a9miJb1QQTp4ubP1mVUUZ8i5cuibdViO6EtNX2UPewcVI2q4jLH9zPo/98SYOzo5kJJ/mi34TrMag71uj8Yzw58jq3ayfuQh7Z0cSZj2OTq+jKLeAn8bOpKy4FKOnC4P+O465g6fQ+60H8YjwJ3n1bjbOtB7bqjb2zo70mzVW1S1k0diZNBnaiagKvw3YOmdTnLiaSd+tIuVsJvHRwYzu0dKqPsDUH9bSPiqIjjGhFWnnL+Xx6Me/suC5oRb2ri8/i01IMIUbN3Ppy3kWx3Ue7nj85zXyN/yNadwYMsY+Q9nFLHRubri/MZGMMU9Wsh807SG8IwJIWr2LVbMWVuunNTsnTxMjP3qS/w55DQAXHzfGLpqCzbFTABSfOY9doA+XVm/j3KzvLX11NhA083mEXkdZXgHHx76FLC4BwMbTldC5kzjc50mLfK3eHY1LfX9Or9rN/umLrPpr7+lCu9njWNV/CgCGAA/afPAIskySc/Qs25773CLPpC+XknLmPPEx4YzuW/3Lvqnf/EH7mHA6NqnP92t28OfWAwBcyisgNsyfV0feXmHr+NBz6AOCKN61mcKFlvUlXN0xPjWZ4p2bcBzxKDmvP41wcMTxvnEIRwMlyUkUzPuvRT7Hh810f65G95nJFO/YhOOoR8mZrOreb6b7taVuORHvjcGxfj0yV+4gbfpPFsf1zgYiP3kKoddRmlvAwYffr6i7K1GbutHvP4xT/QDSV+4k9X3rbdfOy0ST2U+xtd8kAMKfG4Rb22gA7L1NnPp+Halm99bB0x7CJyKAA6t3sfIK10NVOwdnR0bMfAKdXk9hXgHzxs4g4fUH8G8YhJOHC++0H1et1oBpo/GKCODQ6l2sUe+P1tKMni4M/+hJPhsyGbh8TRpMRr4dN4tTiUet6l/Ltas3KeN0pdlnweJPnkJW9iVeef1d8gsKqi2Thsb/Ojf1CL6Usp2UshdwCBgjpexl9ukipZyL0sn+CtgKTAfGoHT6J6J06N8DWgHfoXTuuwOZwKfAWSnlSGC+EMIVOIzyoNAI8AfaA87AcZSHgtFqfoQQ9YB9gJsQYg0wFWgkhLiovlUo5wmgkxCiB7Ae6A30AsYCuaqNCfAAXgIaAueBx4AIIcQ6IA2o2kNLAPQoDwX+7iGWPxgU1bMlOr2O2QNfw9nHFfcQH6tp3g0C+H3KPNZ9uJgj6/bgHxNCk4R4MpJPc3zNHtI2JOIVHYTJyjnCeyl6Pw6YjNHHDVOID55RQRxavImFQ6aycMhUziel0eXtB8k5dZ5Pox9CAm0fvP26/QUIbhWJk5crOhs9Or2O5dPmk7Y7hX1L/64ow7Xqbp23kjnDpjJn2FSObUli27er6PnK3Vw8cY63Ykbj4GIgsqdlR7lhr5bodDq+TLisFdO/HX9/9hvfjHiTnPQsIjo2wcHFwJ3vPoKtwZ6GvVoidDq+TpiMs48bblZi28CKTaP+7djy2e/MHzGN3PSLhHVsws55K/l22FS+HTaVE1sPUnJiHyt3J1NaJvnqyYGcy8rlWPpFC32AHcmnOH8pr1LnHuC9xRsotNKpcegYDzodGQ89js7TA329AAsb27AQLs74iJy531CweRu2kfURzk64TXgB4ehYyTamZyt0eh0fDZyIi48bniG+Vv20ZufoYmTou2OwM1S8NCOoaQSrPlxEyrCXyZjzK7KklOSBz2Pj44FdiJ+Frlu/TmTMXkTqyAmUpGfi3LF5xTG/l+9HONhb5Ano3RKh17Hyztdw9HHFKdSy7mxNBtp88Ag2Zr6Fj+zCthfnsGbwGxj8PTBFBVbKs3LHQaXOXhzFuaxLHDt7wWosdhw6wfmsXDo2UWYhDunUnM+fu5vPn7ub5vUDGdih6WU/WsUjdDpyJj6Ozs0Tna9lfenrhZD/9YcULvqG4j1b0Yc2wGH4wxT8/BU5r41D5+6FTVSTyuVrrepOuIJuYAj5X31I4cJvKN69FX1YAxzuVnUnqbrRTSzyAbj3bgN6HXvveAU7XzccQi3bhdfAeE5+/CuJQ6dQlH4Rt85NLYXqUNe7dyuETseWvhNw8HHDYEXLxmQk5oMx6M3aQfLbP7ItYTLbEiZz6cAJTn2/ruJYeTufNXAiphpcD+Z2zfu1Z+3s3/h05BtcSr9Ij6cGY+tgR+7FHOwc7fGoRita1fp04CRcfNzwCPG1mubgYmSQ2fVW7sOmr5ZRUlxCUa71zva1XrulWacpzTpdbeceQK/X8c7klzAaDdXaaPzvIv/B/25mbuoOPoAQYgAwALhfCLFC/WwVQswQQkQCA4GVwDzgdcABaIwyCn5USlkqpVwC7AYKpJSzpJS/SymnAiVCiKUoo/A6KWUpMAOls/4M8AHKw8GbKA8ZW7k8Wl4E/CGl7GT+AXYBFT0jKWWulHIE0BWYBDyE8kYgFPhGNStFGbF/FziJ8gahDfAK8DDwvZRyS5XQdALKhyRXBbeKtIhdaFw0+5b8DUDKxv0Et4q0mpayIZG0nUcIbt2QgCbhnNhxhPzMSwS1rE/qyl04+XtwdPUe/FtbniOgbRSHf90MQNqGRPxbR+LbPIKwXi0Z+NOr9PhgDEKvwzMqkKSfNwBwPuk4flFBFlo19Vdno+fONx/kYlo6zYd0Yt+Sv0nZkMjeXzbRfEinijJcq245zj5uOHmaOL3vKC6+7mSfvoC9i4Gy0jJc/C1/bTM4Lpr9SxWtoxv3E9gqku1fryB1/T4AjB4u5J7PpqxM8vPYmRReyic4LpqkpZvVPInUs1J/wXFRFjY7vl7BUVXX4OFC3vmsCnsnHzeMniZkdjrbjpykR7NwAFrXD2BnymkL/eLSUiYvWI2/mzOr96ZUpG85lIajnS0ezpZ/PO2bNyF/5VoACrfvxL5JrIVN4dYdFCcewK5pY+yiGlK0dz+UlnFh/BRkbm4l27C4aPao9ZC8cR8hVuJQnV1ZWRnfjJ1BQU5+hV1Qs/q0HdGd8J/fxvuJYWQt+QuA3I27MbaKttA9P+83ctbvAkDvbqJEjaexbWPK8gsoSc+0yOPdNpoTvyi+nF2/Hy8r14UslWx6eCbFly77tvfNH7h0WHmzYOfmRKHZGyaAbQeP0aNVQwBaNwxh55E0C93iklImf/0b/h4mVu86VOnY2cxLnM/OJTr48oOMTXRTiv5eA0DJvh3YRFrWV8m+HZQeOYC+YWNswhtScjgRvW89SlMPK2XJzgRD5WlvNtFNKdpkptvQiu7eHZQePoA+qjE2EQ0pOZSI3q8epSnV65Zjuq0R53/ZCEDW+n24tImysDnz5Z9krdsDgK2HC0UZWRY2danr1i6as79sAuDC+kRc21hrB2XseWgGJWbtoByXpmEUnrlA4ZnLbSw8Lprdajs/snEfodVcD9bsNs5bzuH1ewFwcnfBI8ibvX9sYd7YGeScz8La3wiA0Lgo9i5R7jPJGxPV+6NlmiwrY/7YDyqut7C4aJJW76Lv+BGkJ5+i9bDOVvWv9drVm/zRGa/8q8ZORiPOTv/Y8jkNjX8lN3UHXwjRD6VzfRRldL788wNKJ9oALAeaA9NQ5t4/jNKZjkXplJdzBmhn9pCwAmiLMiVmpJSyfLjMHmXqkg3gKKWcgDK6Pkh9ODih2umAlkKINeYflM55pXnzQojZwEQpZYZ6Pnsp5a9Syt9VExvgNZTpQu8C7wOnUebrBwEpWGIcMWJEnBBiW0JCwhtZzpajrbYGe7LPKn88CnPycfI0WU0rJ6ZvHGUlJZSVlnFs2yEMbs7UaxtF5pFT5J27iMHMthwbgz256h+oopx8DJ4mzu5O4efBU/lp4BQKs/MI6dKUS6cvENK1KYHxMQTERZN12nJ0sqb+Nk1oT/rhk6z/ZAkufu4Et25YcTwkLqqiDNcbhzajurP1m5UAFGTn4Wgy0vrenlw8kY6dwcGq35fUGBReysdophXQPAIHk4GTO49QlJNPofqH3jxPUZU81nSr2ii6Rk7tTK5IazGqOzvmrQAgv6gEb5PSeTI62HHhUp6F/pKtBwnzdefers3Yd/wc363bQ3FJKZ/8uZUn+ra1sAcQjg6UpitTr2RuHjp3N6t2AI7dOiFLS6CsDJmXh8zNxSawHp4fvlfxaX9fL7LUkeqCKvVgjp3B3sKuMCefgiodp4Nrd/HRwIkkJzyHjZszwlGpr9KcfGw8q/fV0DwSvcmJvJ0HEbY2+Iwbxulpc63a2hjsyVfrpTgnH3svS59LcvIrde7NCbwzjuyDaRScvVgpPb+wGG9XZ0Cts+xci7xLNu0jzM+Te3vFsS/1NN+t3FZxbMHq7Qzp1LxyBnsHyi5kACDz8xCm6mNg17YzsrQUysoo2rIWh4H3YNO8LTaNW1Oyb7ulbuY16JaoupvX4jBI1W3SmpK9263m0RvsKVTvEaWX8rGtpl0AOLdogI3JiZwdh6u1qQtdvcGBAlWr5FI+dlbaQWlOvtXOPUDQ6Nux8zTR8ucJFZ/4G7geBr7xAGPmv8qTS94gol0MTfu2JePoGQou5VNWWoaTp/V1EHYGe7JVLeVe6GI1rdfLwxnx2TP4RQfzwPzxtL+vFxHtYtizdDN7/9hC+G0xRHezXGJ2rdduadYphN4W9HZW/dXQuBplyH/sczNz03bwhRCtUEa7B6NMrSkw+xQBUkq5E2UKznigvpTyuJQyD3ABOgLmfz1sgBwpZTeUh4GH1X9nAXnqOfsDT6OMtL8NPCWEuANluk7VIedClGkzA1Hm9/dXR/B7Yrb2QgjRFcXZAnVNwG2o03zMCAb+C9yDMrVoCdAPpYPfFthsJUQ58+bN+01K2fLnn39+sZ6D5SZDRXkF2DooN0k7gz1CCKtp5Syd8CXHtx8msmszuj03hCN/7SXpp/VkJp8mtFszhE5YnKM4txC9g62q54DQCTIOHCfv3EUAMo+cwjXUh4zE46T8sY36d8RxMfUMhdmWHc6a+uvXKIRt360iJz2Ls0nHCWwWUXH80IodFWW4njgIIQhtG03qpv0A+EYHsenT3/jrg4Xkns/Cv3HlqSwARbkF2JRrGe0r4uRgMtLrtXv49bnPqsmjxM3W6GA1tkW5hVZtHExGur82iqXPfXrZWAiCb4vi+CZlLrajvW3FFJv8wmLKrGwblpSWwcC20Xi6GOnTogFbj5zkixU7GBofi4vZq3NzZF4+wl45JhwdwYrf5WS98wFFexNxaBdXkVZyIo2Mx56u+Kyf83tFPdgbHCq1x0qxMKuvK9kd3X6IQnWqQMmFLOzVaTk6gwNUk0dvcsJ/0sOkPT8DAK8xgzj/1VLKrHSwAUryCtCrvtiY1XdNMAZ50XBMb3ZO+NrimKODHYVFZnVWZqXOTpxhYIemeJqc6BPXiK0HjwFQVibZdvAYrRoGV85QkI+wU3wVDo4gqr/l58+ZQemhfdg2a0vhwnkU796Mfec+FP31JxRWmX5xLbpfqLrN21L48zyKd23GvksfitZZ0VUpzS1Ap8ZYZ3RA6Kzr27g6EfbGAxx56sNqz19XuqW5BegdFS290b5aLav6LgbsPE3sHTOzYrrOtoTJ/GV2PdjV8Hoot/vp5c/58uH3kGWS93u/WElL6HSIauqoKK+wipbOatrilz/n82Gvc3r/MT4f9jrr5/yOT/0ANn69jLLiUs4eOkFYnOUbkeu5dmVpkdLJ19DQuG5u2g6+lHKrlLIPyuLTcOBFs8/dZqa2gBHIE0KMUtMygHQpZUkVu3ImAXp1h5pS1MXGUspFwEaU0fQFwK9Syl+B2wFvIYT5ZNv5KHP0nwaSUd4sgDK//lMAdQvOqShz61F9/xZYL4R4zaysG1Cm25RPNfpGSlkM7EDp6G+1EqLt6vkBmmSmZVgYnNqbSnDLBgD4RgeTmZZhNa39I31pkqBIObgYKMjOxdbRjrwLlwho0xDfZuE4+bmTnZZucY70vakVU3c8o4PITkunx/QxeEYFIXSCsF4tydh/nPS9qTh6mPCIrMf5A8e5EX8vHDuLW6A3AMX5RQidTi1DPJlpGRVluFZdgODWkaTtujy9pyivQJ0vL/BvHE5eZo6F32f2phLYStHyiQomKy0Dna2egR89wappC8g6aVnWM3tTK6bl+EQFkWUlttZsdLZ6+n/0OGumfU/2yfMVtoGtIyuN5kfX86qYlnPwVAb+7pajd4GeJtLOZwOw/0Q6fm7ObD50ggV/7eWBmQs5eDKD1+avqpSn6OChimk5tvXDKD19xkLXacQwHG9XnmF1Tk6U5VjGrJy0vamEtFTK6BcdTKaVOFyL3YNfvYSzlyvCwR4bD1f06lsMx+hQitPOWdgLWxuCPnyBM299RfFJRdOpXRM8RvUhbP4bOEaHUu/NxyvlydyTimdrpb5do4PJPWFZv9awNRlo+9+xbHnqM6uj+9FBvhXTcg6eOIu/ldHbQG830tT1FPuPnsbPQ7HZcfgEMaH+FvYlqYcqpuXog8MpS7esL/s7hmEb30OJh9EJmafUV+nRIwhPbwqX/nB9uncOw7bDFXSXWOqWk7MnpWL6jLFRMAUnrNdd5KdPc3TqNxRauZ/UtW727hRc1beHzo2CyT9hvU1aw7tXSzJW7rRIT9ubSqjazv2vcj1UtdPb6hn54Th+e2s+mSczKtnYGx2q1Tq5N5XgimsriMy0dKtp1nyQEjwCvfGLDsbO0Z5MK/e6a712QaCzNSBLi6zaaWhcDSnlP/a5mblpO/hV2Cyl7Fb+QdnxRgohWqJM0XkB6A+cE0IsBg4CKUKIL4QQ5SvZUoA3hRCfo3ScnYAtKFtnngAQQvRGeVgoBIoBLyHE/SgPB08DX6nbZvqrNheBEinlSuAJIUQIytSh00KI5igLar8DbIQQcwF/KeUHUsrpgIcQYqEQIkz172OUDv54IEYIEYqy2LcIZQpSVRYBI1HeYAw5dziNrs8MrmSQtGw7TRLa02v83cT0acOh1Tutpm37dhVNB7Tn/gWvotPrOLJuL3999AuNerem7YtD8WkWgWuYLxcOniTuuUGVzpH853YaJrSj/YS7iejbhqMrd7FlxkK6z3iEYX++wZkdRzixPpHkP7fT5P4eyLIygjs34Ub83b5gDaFto7l/wav4NQrB4O6Eyd+TiPgYbhvdm8AW9Tmybu816wJEdGjM0c1JFXn/nPotzYZ14qVDX+Ia6MmuBWvo9Kyl340HtKf7q3cT3bcNh1ftpNnQTvjFhtJ+bD9Gzn+F6L5xFnliBrSj66t307BvGzIOnaTDs5Vje6iKTfKqXTQZ2gnf2FBuG9uP4fNfIapvGwDCOjTm+JbLfnduHMbSbQd5Z+F6lu9MJtzXnVnqOoFyBsRFsfXwSe7/4Ge+X7+XUZ2b8sUTCXz++AA+f3wAkQGeTBzWpVKegrUbMPTqjumJMTh27URxylGcH7q/kk3u4iUYenXH86PpoNNRuHkb1ZG4bBvNE+LpO34EjfvEkbR6J94RAfR8ZshV7ayxYsZPPDx/PBEL3ybji8U4d2yO3/gHMPVpT8HhY/g8M6KSvdvQ7jjGRuA9dghh89/A1Lc9KUNfImXYy6QMe5n8/amkvTizUp6037cTMqg9TSfdTdCdbcg+mEbMC5XbhDWixt6JIcCD5lNH0fmnV/Bq27DS8c7NGrD07328s2AFy7clEe7vyayFayvZDGjfhK0Hj3P/W/P4fs0ORvVQ6n9jYgotGliuayneth67+B44jHgU27hOlKYdxWFI5foqWrUEu/juOE2YDjo9JXuU8QSHO4YpnfuiQkvdreux69ADh5GPYttW1R1aRXelqjtJ1d2t6t5ZvW45F37fgvegDoRMugfPO24j7+AJgl4YVsnGZ3gXnBqHEfjkQGJ+fg3PfrdVq1cXuud+34b/4HgavDYSnzvbkpN0gogXh1i1rYpH5yZkqm/bzNm3bBstEuK5Y/wImvSJ48DqnfhEBNCryvVgza710M7Uiw2l29j+jJn/KkInKmyMHi4cXL0Tr4gAulW57x5Yto2mCe25ffwIYvrEcXD1TqtpVUlctg2jmxP9p9xHx4f6YnBz5tj2Qzd87dq4+lNWkA2lxVeN45ez3rqqjYbG/yriZn8CEUJEANOllH3V761RRsGfBf4EfKSUR4UQNijTauZLKTertvejjHS7oCyYTQa+klL+oh43okwD6grciTKn3xVYiDLa/hewFBgupUxX3xCUAGtRptWcVG3K58j7A1OklJXevwsh+gJOUsr5VdLvAvaj7NLzX1XnDSAaeEst41ngR9WHZCrjhjLdZ92EkLstV1GijMiHx8dybEsSOelZ1aZVRz0nI4HxsZzanEReNbb2JsNVbaraHbtw0arN9fhbk/LcSBycnQ2EqXa5V9C/mk1VnFyMhMTHcuIqulezqcqTH7cGIDuvgE0HT9Ai3B9PlxtbkHZh8qKKfwtnJxxat6Bw5x7KLlguQr0WZpz2wdHFSP34WFK2HLhiPdTUDmAEymix3sWIU3wzcrfso6SanYSuhQOFypsQW5MB3w6xpP+dREEN66U67lxwecZedm4+m/YfpUWDQDxNN/Z7DoX/VdYQCKMTNrEtKTmwG5l1Y/UFVExArG3dxLWVF1bqTUZcOzYme9MBimuh7mpTN1cqO0zbmIx4dIwlc9MBim6wHQAscxQ4uhhpoLbzS1e5Hq5mV26TdpV7nIOLkYj4GI5Wuj9applTjKyTa3fqtqlXPH692HqGXd1I43qp+TzFf4DO9br/Yx3b1WnLb6qym3PTd/D/VxBC2JRPKVLn6pfv6oMQQsirVNSEkLvrpCK9Suum7abr/z3tzraOXLWpo3tieQe/NjHv4NcmM05bbjFZG5R38Gub8g5+bWLewa9Nyjv4tU4d/bpL1Q7+zUx5B7+2WeZYN/cE+zq41xTX0QJDrYP/r+Sm6uRqHXyFm/qHrv6XMF8voHbmS6t819DQ0NDQ0NDQuAI3+/70/xT/ljn4GhoaGhoaGhoaGho1QOvga2hoaGhoaGhoaNxCaFN0NDQ0NDQ0NDQ0bgms/e7L/yLaCL6GhoaGhoaGhobGLYQ2gn+L4CTrZiF3ir7k6kbXgTP6WtcsraOFNWV1tEZ+VYnlj+zUBjH3bap1zUR7yx9Rqg2EKL260XXwH1n77Qsgyq72NdsM+aT2RYH9F47Xie403851optn/+8ZdTslrr5H+/XgI+vm11vr4h5WV1uHbGj0Qp3otkucVie62u48Nx//njtJ3aKN4GtoaGhoaGhoaGjcQmgj+BoaGhoaGhoaGrcEZdoYPqCN4GtoaGhoaGhoaGjcUmgj+BoaGhoaGhoaGrcE2gi+gjaCr6GhoaGhoaGhoXEL8T83gi+ECAROAm6APVAGXJJS5l6nni1QIqW28aqGhoaGhoaGxv8nWndM4X9qBF8I4QL8AkQCMcAzQBPgdSGEsxDCTgjhIoToKYR4QwhhEkIYhBDjhBDNzHSEEOI7IYQj8CLwhxBimRDiDyFEvhBitJnti0KIgKp+CCG2V0mzNfv3OCHEMLPvdfYg5mAyEtw+Bkc3p7o6xf88jiYj4e1jMLg5/3+78o/jYDISorUvC5y8XQlt1wg7o0Ot6rq4uhDXoRWu7qbr1nBzc6Vb13g8PNxq0TONW41b9b6WcSGTUWOe/f92Q+MWQQixQgixpprPvLo89//MCL4Qwgf4FliBMmofAAxCGcm3BSYCs4FhgC8QDjwFrFHT9ggh2gOnpJQpQojNwD1Syinqg8OvQBLwjJTyM7NT7wbmCCGeBpqqaTrAJIS4D1gmpTwJbBRC5Ku+BQHHhRCPoGw3bBBCdJBS5ldXvl5vPYhHhD8pq3ezaebiGtkYvV3p/8k4klfupMurdzP/rjfIv3AJgO6v30vK6t2cXrWVodMexicigAOrd7J81sJqY1zVzsHZkVEzx6HT6yjMKyA/Ow//hkHYGx3Iz8gmbXcKf079ppJGv2mj8Yrw5/Dq3aydtajaNACjpwuj5r7Af/u8Qr9po/GM8Een17Fm5iKSVu6w6uOAaaPxigjg0OpdrFG1rKUNnvEYDbs0469PlrB21iJM/h4Mem8Mskxy/uhZFr88u0ZaTl6uDP/kKQ6u3MHt40fwxfCp5KkxdvN0Zdq8//BQrzEAPPfO0wRFBLF51RbmffBttXF283Rl0icTGDfwaQDufWYUTeIaA+Du5Ubugo0cmbmYJu89hFP9AM6t3MXh6dbrzc7TRMvZT7Kx/2sAOPi60f63KeQePQvA9tHTKTp/idvN2s7GatpXVRujtysJn4zjiNq+vjNrXwZPF4Z+9Tzv9XmJQdMewjsigKTVu1h1hfZlzc7J08SIj57k4yGK/67+Hgx571EKyko4c/QMs1/6qJLGQ2+NJSCiHrtWb2fhzB+qPZfJ08ST/32B1wa/XClvREQQR1bv5q+Zi6zmu+MtpR2a2xg9XRj833F8OXgKAN4NA7l98r2kbkik28t38f3toygpvvx7E5Pee5mw+iH8tXIjn03/0up5nJyNvPXJFPR6PXm5+Tz38Hhc3Uy8/8V/WLd8A8+9No74rv3JyLhQkefTT94hqmF9fv9jFW/8Z4ZVXV9fb378fjZLf1vB229PpHuPIQxM6MuQwXcocXE1EWh0o/BiDimrd7O5mrbQw6wtmNsYPF0Y+NXzfN17fEVa19fvxTXUB73BniOrd7O+mtj2NYvt+pmLsHd2JGHW4+j0OopyC/hp7EzKiksxerow6L/jmDt4ikWe2tIFGDHtEXwjAkhcvZPfZ/1sVduanU6vY8q6WWScUK6xBRO/4EzyKe57fywNWkZhZ3Rgy9xlrHrHevu8863L98N1Zm1syH/HMUf1zeTvwYOLX8PWwZ7zqWf4pN+rVrWu5X7bd8q9HF6zm4Y9WuIbFYTR04Wt36y0uK9Bze+zRk8X7vroSWYPmQyAs48bbRdOJj/1DI4R/hSeOs/5P7dy7H3r8bX1MtFo9jPs6jcBAKfYUMInjEDnaE/6kr9J+3hJtfVijazsS7zy+rvkFxRcUz6Nm4ubbA7+m1LKFdYOCCH6q///HIgCfpNSvl6NrQmYj9JvzwGGSimLrnTi/6UR/FbAdygd6O5AJvApcFZKORIlcOlAB6AR4A+0B5yB4ygPBKPVvAAfAJ8IId4E3gCKgY5AtBDiu/KTSil/B+4BcoFzwBnglGp/FihUTf8A3gbmojyIfAVMBebA/7F33uFRVG0fvs9u6m56JQnpgZAQCL1HgghYQDoiKsorWLF8iooCilhBUCl2bNhAbCAWOtJ7Cx1CKKEmENJ7zvfHTJbNZhOSkFWRubn2Invmmd88c9qeOfPMGf6sbnAPDBA6Hd8MmISLvyeeYf6VDBrd3AZLG59GQayY9DUbZy0kZfVu/OPCAGjYNhqjrzvJy3fQrFdbdHodMwa+iJu/Jz5hDaw6YM2udd8urJr9Gx/e8zr2To54Bvpw4fg5zhw8yc/PfoxbgBdhHWJMGjG92qDT65g98GVc/T3wCvO3mlZOrxfuws7JwWSz+M3vMHq5kpZ82qqPsaqPHw+ciJu/J95hDaymtejfhfD2MVw8fs6U1nZYdxaO+4zPhr2Ge6AXne6/GaHu52qmZZnm37ghv7/yFX+9t4Ajq3cTGBdu8uehCQ/i6KS8OSnhli7odHoe6/ckPv7eBIUHWT0HF3cXxr77LE6GyzO/X0ybw/8NHsP/DR7D0QPHSJ2/mga3Kr6s6/MSTg08MYZXLjd7dyMtZzyMncHRlObRKorD039hw4BX2DDgFYouZNPg1rbodDq+rqZ+Nb65TSUbn0ZBLJ/0NRvU+tVArV8AN44bhp2jA03VPHt/4EumvLaGNTtnNyNDpj2Mg5n/7Yd155dxn/LqnS/iHehNSJNQ07a2N3dAp9Px0oCxePp70SAswOqxjG5GHp72BI5muuX7fj7gZVws6mE5TdQ2Zm7j5Gag77SHsDfT8m0UxMIxH7F6+k9knEijYcjll4h1v7Urer2O4X0ewK+BDyHhDa36eNvAXsz58DsevOMJLqRdoEu3DkRGR/DWi9P5ZPqXrF+1iVYtm5ns+/W7Bb1eT0LXvgQE+BMVFW5Vt2lsNE+Pmcgbb85g6ZK/aNWyGR99PIfuPQbTvcdgTp8+y6VjZ/lOLWcPK/kQdXMbhF5n1abr+GHYOV1+W1hQu2j84sLIOZvBFwMqt3HzvNXpdBVs4vp1ZuMnv/PN3W+Sk5ZJVNd4nNwM3K7mt7V96kMXoEWvduj0OqYOnIC7vye+VdRba3ZBTULZ8us63hn6Mu8MfZnTB08Sl9iCnIvZHF27hzXvLSC0fYxVf2NUfz8189fJzUD/aQ9VaAc9J9xF6o5k3mw2CqOPG41vbFFZqxb9bWjbaFx8PdDZ6dHpdSydPJdTu46y57dNlfo1a/2gtTQnNyMDLdpvcIsojr/7E6mzfydj5S623/w8Dv5eOFvpv+zcjcTMGI3ebP9Gr/+PA0+8z47e4/Ht3QGnED+r5VIVer2OqZOex2g01Go/DY26IoQYAOillJ2AQCFEoypM7wLellL2QBlH3nwl7etmgC+lXIQym14gpZwlpfxDSvkaUCKE+A34BOUFaNNRBtZPowzi2wJvAoeALcDvQojhwEogCiXc509gObBO/dsHQAjRRwixBnhZSpmCEs6zSb2ay5RS/i6lTFddfB04AjwM9Af6AmuALGDiFU4v8eBvmwA4vn4vQW2jKxmEdIjB0ub4ur2c2ZFMw3bRBMRHcnr7EXR2enq9eT+ZqelE9WhFVIdYdi5S3ox6ZP1eIqxoA1bt1n29lENrkwDwCfVn34rt+EYEsOv3jYS2jSY3PRMn18sdaXiHWPYs2gjA0fX7CG0bbTUNILxjLMX5heSkXSK8Qyz7/thM/zdHce7QKTrc29Oqj+EdYkhapORB8vq9qn7ltJA2jflt0hwKcvJNacumfm+6cDB4uOLXOJg96n5HzbQs05LX7SF1xxHC2jWhYXwkJ7cfBiCiYywFeQVcTMsAoEXH5qxa9BcA29fvpFnbOKvnUFZaxqSHXyUvJ6/Stuj4xqSfTafgbAbenWI5vVDJt/S1e/BqX7ncZGkZ2x6cTnHO5WtHz9aNCLuvB50XvUzTl+8BwLtTLPvN6k7DKuqXpc3xdXs5vSOZ4HbRBMZHcmr7EQBCO8VSnFdIblomkR1i2a2Wb/L6PYRXUb+s2ZWVlfHt6OkUmPm/eOr3nFfLycXDleyLWaZtsR3i2PjbOgD2rN9NdNsYrFFWVsb00VPJz863uu+x9fsItuJnaIdY9v22sYJNWZnkx9EzKTTT2vvrRi6dSqfRjS1wdjdwIiXVtK1Np1YsXrgcgM1rt9GyfbxVH+d98RMbV28BwNPbg4vpGWxas4Xd2/fSukML4lrEsGHj5SjArjd05IcffgVg5ap1dO7U1qru8hVr2LR5Owld2tO2bYsKGoGBDWgUFc6urxT/TlTR1wR3jOGQ2g7MbYLNyh1AZ6en55v3o3ewI/vUhVrn7bavlpGydg8ARm83ci9kUVYm+UnNb2v71IcuQOMOTdmm9ncH1+8hqm0Tq/lpzS68ZSNa9GzL0/MnMeJd5U7B7mXbkGWSvb9txOjtxundyYRY8TesQyx7VX9T1u8jpG00skwy36KO5Zy7xPbvVgBQWlRi9cK5pv2tzk7P7W+O5FJqGq2GJLJn0UaOrtvL7oXraTWka4V+TdGt3A9aS5NlZcwbPYNCs/Yb3DKKwBG9aPTmSPRuym/DpbV7cG9fOX9laRl7H3iHUrPztvNwofC0UpeKM7LRuzhbLZeqcDEacXUx1mofjX8f8m/8J4R4QAix1ezzQC3dTQS+V/9egTKxXPmcpHxfSrlU/eqLMmFcLddNiI7KWaCzEMLydok7cI+U8qIQwhEoVdOdpZQvCiFuAnpKKZ9V0+cIISIAJ5SZeBfAGeWhXfNg49+B34D16vdtwD1AxbgBhSbAHcBTwEModxc+AS4BHsBnljuoFemBefPmhW48uosGQFF2Pp6hVmZrDY5kn1UGk5Y2Tfp0oKykFFlaxpBvxuLs5UJgy0ga39IW9yAfNn2/EoCCnHx8rMwqATgYnMg8d9GqXWirRjg4O3J4/R4cnB1pelMbdMWlhLRuzLIp8yr4mHVO8bEwJx/vMH+raX3fvJ/YW9tz/mAqftENCe8QS96lbM4fTiVp0UbaDetOh3t7svHLJRY+OpKl+liuZS1Nb6fnQspZU5r5ucT17sD5w6kIqJGW+X6lJaWUlZaht9cz6N1HOX78FOHRYbwzfyotOsbz+3d/ApCXnUdQ2OVZXXOsDezLGXh/f76YNoehgJ3BkYIzii8l2fkYrfzAl5j9sJZzfsVODr39E6W5BbT7+llcY0KwMziSo9adwmrqV1U2Tfoo5y5Ly7j5zftpcms70g6l4tu4ISEdY9hiVr+qmsE3z9tyu0Ir/pfToXdnDK5GRs942pQW2zGOlfOUpp+fnU+DUOsz+PlWdB0NTlw8ewGjen5eoZXbgYNZGyu3KarCRwejE7G3tcejoS+f/DDTlN62Uyt+/lYZiOdk5xIcZn0Gv5zmreNwc3dj9/a9prRefW+iYVhDFi6YQ1mp0pV17dqJz7+YC0B2VjZRkWHV6g4efDvFxSWUlpaa0h55+D6OHDlmKuei7Hw8rZSXvbNjJRudvZ6OT/Rnwah36PvJ/wEQO7ALFw6foqSwGKO/B23v68mF5DN4Wslbe4u8NbcJahWFk7uBUzuO1Hif2ure+vr/8I4IoEFsKPfMHUdYx1jWfa8MoAty8qucwXcwOHLJrN76hjXg4Lo9vH3HRLLSLjF00v3EdWvJ7mXbTDPZ4Z1iWf7WfBrEhFj1N8uijllrB+V2TXt3IOPEeewc7a3b1KC/bTGgC2mHT7H2o0Xc8+VzhLZrwuFVuyjMySe8QwzF+UWUlZZVOOea9I3W/D701y6Y8j1Rr47AOTwAY2wIJdl5VmfwS63sn7XlIEH/u5niSzk4BfuRu+94JRsNjfpESvkxynitrhhRFn4BZUI3qjpjIURHwFNKufFKwtfbAN8OyJFS9hNC9ACOSimT1QF/nhoP9ZRqZwQyhRB5KHH6lXtbZcZfACNVG1eU+H1fACllKYAQotz+Q8DNUkSNrXoQeAH4FOWC42mUi4fxwJ1CCAfLeCuzijW98Nu9j585noy90QmhE1hSlFuInZPSyVvaLJvwJV2eHkRk9xakHzzJpvd/JeWv3XhFBdLv06ewV2+pOxgcEcL6TZ+ivAKrdgZ3IwMn3seBv3Zi7+TA0lk/c9Oj/WjbL4GdP66hKK+wGg1hNS3z9EUWjp3N3t83M2LuOM7uP4F/42C2fLcCeydHzuw/TkTH2EoD/KK8QjMtJ4TQ1TgNwDPYjy6jbuPzu1+n+1ODTaEG5TaFeYWV0spZ9OIXdH9qMNHdW+IbEcBvL8/h44WLeGf+VP5v8BhGv/wIDs7KD7yz0RldFflcFUY3Ix7enpw+fgbwoSS3AJ3qi97oBFbqhDUythyirEiJB885chqXiAaU5BaY6o5DFfWr2Kx+WdosnfAlCWr9yj59gT/GfsrB3zczbO44Thw4ZpHX1v0srFAPqrYD8Ar2o/cD/Rjf9xnysy9fEA1/6X4cVA0noxNCV/M8LsjNN+3rYHSsoo0VXC7/KmxM55OVx4KnP6LfOw/z7iefk7RjHwDPvfIkTk5KPTAYndFV46ObhxvPv/4UT93/QoX015+fSmZGFqu2bWb+/IUAvD3tZZydlLAuo4uxWl2Ax58Yx8sTn+G223owf/5ChBAkdu3E5i078DHrR7BSDsV5FfsahKDdI33YOWcphVmXy8OvaSi7v11JeLd4Tm87TFSf9mSfvVirvHVyN3Lzy/fyw0OVnymoSXnUVPf3F5T5lXvmjuOroa/ReeJdpvrgaHCqsr0W5hVUsjt14Dglahs7m3wKXzVUrLigiBufGcKvz83GPci7Sn/ta1DHinIL8Ar1o9MDt7Lx88W4B3pXtqlhfxvQNIyt360gJy2TcwdOENwySt3uxMFlO7BzciC6e0vTzL+1frC6vtGcE9sOEZlbQGluAYVn0nEODwApa9x/HRzzMZ5dmhL27B2crOKZC43/PtfYKjo5KBPEoEwQV9k5CyG8gJnAwJoIXzchOirm0xgTAb0QQo8yY28npfwFZbb9ZWAe8KuU8lfgFsBPfVDXEjsp5U0oA/KPUR7IPWft4FLKZGCXECKAy7H3oBRWOMosvUC5QAhEudCYDiQAw6s5r23lYRN+MSFkpqZVMjiXlIKlTbuHetN0gHI3yNHNQEFWHhnHzuGuxi02aBZO+vFzhLdRbo8GxYZy0Yo2wMmko5Xs9PZ67n3vSRZNmcuRjftM2x2NTtg7O7B+9u8VNE4npRDaprFy7NhQMlLTraZFdI6j3fAejJg7jgaxoTSICUHvYIdXsB8BsSHoHey5dCodS04lpRDaRsmDgNgQMlLTapzm5GZkyMzR/PzsxxRm56t+Rat+KTbW0hIe6kOLAQkAOKl5HNk5jg7De/DO/KlExUYy5q2nOLT7EM3aNgUgMjaCs6lnrZd0FXTp2YlNKzaZvmfuTjGF5bg1DSX/pPVys6T93Odx9PNA7+yAb2Jzsg6cJHN35bpjyVkr9av9Q72JU+uXk5uBwqw8wrrE0Xp4D4bNHYdfbAgBMaGEqXkWGBtKRhX161RSSo3snN2M3DnzMT56ZlaFwT1ASlKyKSwnNCaMtNQr3uG0uq9/TCiXUivXrzNJKYS0bVytDcCtr44gpJ3SFpzcDGRn5Zi27dt90BSWE920EadPnrGqYWdvx9SPX2X6ax9wRq0rI0bfTZ/BtwDg6u5C5qVMk/32HUl07qyE5cQ3j+XY8ZNWdZ8Z8wh33z0IAHd3N5NGQpf2bNq8g+07kkwhN36xIWRV0ddY2oR2iaPF8B4MmTcO39gQek4eyaXjSl9zLimFyJ6tuJSajn9MKJlW8u1sUgrBZnmbmZqOzl7PwPcfZ8XkeWRaae/W9qkPXYATSUeJVPuzhrGhXKiiLlmzu+/txwiKCUXoBC16tePUfmWWOaRZBBeOnuF0UspV17H0I6fp9eI9LHjmE7zDGpBhxa6m/e3F4+fwDFZ+E4rzixA6HV0e6k2LAQlq36j0axV1r9w3WuO+OWNx8PMgZ/9xPLrEkXvgJC5NwyioYf9FWRl5R5QQvXM/rqnZPhoa/yzbuByWEw8cs2YkhHBACeV5XkpZo1tT19sM/lHgTfWJ5e0oV0ubUQbWJ4UQt6LM3BeizJ77CiH+h3Jh8BRKaE5/dXtLIFTVAeVB3FKgI8qqOY5SykJ1/wZCiFuApSgx+18C08qdklJ+hlkIjhDiSeCslHJuDc/rl6b9O+Pi70lEYnMWjn6PLmMGsXbqDyaDw0u2MWz+eJPNV/0mIoSg7/uP0XxoIumHUjm2OonT2w5z81ujiOnTAZ29njlj3mf4e0/i7u9JTGIL3u0/Hv+oIFr17cwf07436Sct2cpj8ydWsGt/x400bBZOj9H9EXodngHeuPt70mZAAhs//ZMbHu3L8mmXV4o4sGQb98+fgKu/J40S4/m4/0sgqZSWtHC9aZ8Rc8fx7ci3GfnjS3T63y24BXhx7mAqi9/8lpueHswyM/39S7Yyav6LuPp70jgxng/7vwiSKtOcPVxw8XHn+8dmMvzzZ/AI9Kb3y/cCsPrDhXQedStuql8fqfuNnP9ihTQhdAx973Ha3JHIuUOpHFm9myOrdwOwriSNd+ZPZeozb2NwMTDjp7fx8femXbd2PHr7Y4Q2CqF7vxv57K0vrlgB2ia2Yd6Hl8/17B9b6bTgJZz8PfG7sQXbH5pB9HNDODj5+2pU4NC0H+n043jKiks5PmcZuclnKDx3idYLX6pQvxLGDGKNWf06tGQbd1vUL4Sg3/uPET80kbRDqaSsTiJldZJpn2Fzx/HlqKk8NF95cDY6sQXv9Z+AX1QQLfp2ZolZ/dq7ZGslO2skPnI7HoHe3PeyslLtD+98x/5NSvjK1iWbeGn+63j6e9EisRUT+j1HUKOGdO57A99PrXrVIvN9o/z9iUyM56fRs+g2ZjArp1asv/epdTUyMZ7P+r1kVWv9R4vo987DICXJa5I4lnzCtG3FH3/xxYIP8fX3ocuNHbn7tpFENA7j1v49mTX58l3gAcP6ENs8mlFP3seoJ+/j+y9/4oevFjD141cZMKwPRw4eZcnSv0z2Cxb8yaqVPxMY0IBeN3ejc5c+xMQ04s6h/XnxpSkmu09mf8Pcbz/k/hF3smfvQZNGz56JrFm7keXL1zBlzVMY/T0JT2zOb6Pfo/OYQawzqwtHFm/jjh/Gm2y+7TeRAws2mLYPmTeOJc/Nxt7oRK+po3Dx98QvNpSkX9YRldicn0bPInHMYFZVkbdRat62vCORgGbhdBndly6j+7Lt6+XsW7Sxyn3qSxdg15ItPD3/Zdz9PWma2IIp/cfRICqIdn27sHDavGrtTh04wYjpjyOEYPeyrRxYl0TTxBY0jA2D6BD+b8MM9PZ6vrpnMjeOGVxhNZ0DS7Yxwszf2VXUMe+IAFz9PLjri2dw9nDh94lf0v3pwXXqb2WZpN+UB2jWpyN2jvY4GB1xD/QhKiEO90AvAH6b+KVJd/+SrZX6QWt9ozVWTP+JO3+aqISWlZYROLwHXje2YN+D7xI+digpb175JzF87FCOvnJ1qw9+MWvKlY00NOqHX4A1QohAlMnkDkKIWGCYlHK8md39QGtgnBBiHPCBlHJeJTUzxDV2K6POCCESUB6aTQbmSCkXqulG4AGgOzAMJYTmZ5QHYtegxNAPk1KmqQ/XlgB+KCE5a1Fm7INR4u/tUS4avFEG8MdQHqB4FhgDDEGZoZeqrRPwkpRyjoWvzwGnpJQ17qWmN3tAhiU0I3XzAdNDbJY4uhm4ko0lZ0QJzm5GohOakbx5P9nV7HclO/PtpOVYUVBmNCMTmnF88wFyVA1radb2C0+I41g1NoqdkSgLuyulVZVXNdWqinUlFWelXNxdaJPQil2bkshQH76tC0+V+ADKKjk+XZtxccN+CmtY3lVxxNeV8IRmnLxC/bqSjSUZohRnNyONEppxdPP+avOspnYAJ6pYdMroZqRZQgv2b95LZtqlGvlovu/NCe05Xm2dMBCh1tWa5sEPhUcrfHd1d6Vj13Zs27CDC2kXq9jryuy7eKLCdw8Pd2666QbWrNnIuXM1nBG1wrvRtxGa0IzUTQfIq6ouuBuuaGNpH5AQV+95W5N96qJ7WhRjcDPSJKE5RzbvI6ua/WpqBxDq6l6jvu5KNpZ2Wek117qSfvn2lGra4dX0jTflFwPKKjmeXZuTuWE/RbVsq9bovHfyVWtYw94nwia61xg1i6H6m2gV0OVvG9huP7O22nMXQsxHDdu2wk4p5ZNCCE+UFRpXSylrd/u+umNfLwP8+kR98VSZlLKsBrb68lh8WzIl9G6bFOQZUXJlozrgir7eNUtttPatrXouywF+fVE+wK9P9jrWf3mBMsC3BVUN8K+WGOp/hQ3LAX59YTnAry8mN+hmE908ce38Fp0WxTbR9ZeVH4atD8ps0InZaq3x8gF+faMN8G2KNsD/F3K9hejUC1LKGo96/47BvYaGhoaGhoaGxjX3kK3NuN4estXQ0NDQ0NDQ0ND4T6PN4GtoaGhoaGhoaPwnsFX42LWGNoOvoaGhoaGhoaGh8R9Cm8HX0NDQ0NDQ0ND4TyC1GXxAG+D/Z8ix0QoU9jZ6ON4Wt9Bs9Si7zkbKne2qWjnr6jhUize01hRPWz0qrrfN6jzBZS420T2lv+LCWbVmjD6y3jUBMhrYRndYtPWXZF0tHt99bhNdWzCjlfV13K+WTJ1t+nFb9LdFNhpEFdhghTWAN1tbf3fG1TJ22yv1rqmtzKNRH2gDfA0NDQ0NDQ0Njf8EZdoqOoAWg6+hoaGhoaGhoaHxn0KbwdfQ0NDQ0NDQ0PhPoMXgK2gz+BoaGhoaGhoaGhr/IbQZfA0NDQ0NDQ0Njf8EWgy+gjaD/y9ECGEQQjT4p/3Q0NDQ0NDQ0NC49tAG+FYQQvxPCLFPCLFMCLFfCLFX/XubEGK6FfsEIcQzVWgNF0I4qX/3EkKMrsJOJ4QoX4+xCzDVbJt2p0VDwwwndyNhXeJw9nSpNk3jn0W4umLfqg3Czf2fdkVD4x8l/WIGwx8e80+7cV0g/8Z//2a0gaN1SoC3pZSzhRAPAQVSyi+EEInAbVbstwH3VKF1HvhWCDEGeAMoEELcg3JxlSmlvEm16wU8ow7ySwGEEKsACeiEEDdLKfMttD8FYoDfqzqRvpNH4RsVyOGVu/hr1i9W09re3Z243h0AcHIzkrrzCEvfnMug6Y9ib3TC6OVKXmYuB1buZMWsn6vMtEGTH8AvKqiCnYuPO/e8/yRpKWfwiwqipLAYoRPoABdfD3b8uJrV7y80afSfPArfqCAOrdzJKtXfK6V5BvviGxVEyub9hLaOZvaQSQC4+nvy0C+TuHj8HD4RAWSeuciBpduq1HV0deaOmY+h0+spyitg3ugZlBaX0nfyKBo0CcatgRdvtbd6fXbFfPYI9jX97ervweFVu9i/fHsFjbqce3kaQJ9XRuAd1gCDsyOntx/BKzIQB1dnzu5M5q9XvzXZ9ZwyEu+oQI6u3MWmmQtM6QYfNwbOeZavbh2Po7uBW6c/goOLMxcOpeJop8cjKpATK3axY8blfczpOnVkBZvYe7oTebtSrxzcDJzfkcyFvccrpAlHe/Izczi6chfrZ1rXvcXM3/UzF2D082DAR09wZPkObpxwF9/d+To6O32FtIz9J3AL9uX4yl1srcLfG98aiWdUYCWbrq/dx/GVuzi2bIcpbdiKySAEW39Zy0qzPLdkwORR+EUFcXDlTpOdtTSAvq+M4OCqXfDnToRex4ANb5N9PA2AwswcnH3dObViF0nTrfvv5ONG14+fYPEA6+tw3zRFOb9jK3expYq8tbQReh0j1r5N5gnFj1UvfsmFg6nc+furFGUr3Y/9L7Mp3r4Vl6eexS4klKLNG8n79qtK2jovL9xeepWiTetxeehRLj3zJDI7B6+vvqP0zGkAcmbNoPTY0Qr7TXjjHY4eO8kNHdvy4H13VtJNPX2W195+n9zcPJrFRvPMY6OspllSn7pVtSFzatrOhi9+A0dXAxeTT/NBP+vr69ekHy/H6OPGY0unkH70DCe3H8Y3MhAHF2dSdyXz52vfVNC92j7nzumjie3RmpXvL6zQ59//5Vim3/a8ya6634YPhrwMQI//G0RE+xg8S8HBz53S3ALKiktJW76Do+9Y/91x8HWnxez/Y3PfiRXSW80Zw+EpP5C955gprfeUUfhEBXJk5S7WzvwFa1izMfq4MeiDJ/hysNLOPIJ96T15JPbOjhxZudOqjiWZWdmMe3Ua+QUFNbLX0KgPtBl860iUwfYq4ClgrPr3u1wefAcLIdKEEGuBJUCsEGKt+ikQQugBpJR/Ap8AXsAQYDmwH7gLMI0WpZR/SClvBJyAu9SB/2TghJSyq5XB/QBAD3QCAr3C/CudREyvNuj0OmYPfBlXfw+8wvytpm35ejmfD32Nz4e+xvHNB9j67QriBySw6+d1rPn0d/QOdiyc+CVu/p74hFmPHIrr1RadXsf7A18y2Tm7Gblj2sO4NfA0bTuffJofx37Cp0Nf5dyBk+z4cY1JI7ZXW4Rex8cDJ+Lq74l3WIMrpoW0icbJ3cicEW8Rf3tnnD2MJr3gFlH89d4CNny+mMOrd/Nh3wnV6sb37cy62b/zxT1vkJ2WSaOu8cSq55WWfAY7Jwfqks8hbRrj7G5g9sCXCWwWhleoPweX76igUZdzL08DCG0bTUBcGFnnLvLdgElE9mzN3vl/MW/QK7gGeNGwQwwAUTe3Qeh1fDdgEi7+nniYnU/X8cOwc3JQ/BnQhf0/r2PeoFfwbhSIk7cbC/pNwujviVt45TwIv6UNQqerYLPvq+X8Ovg1fh38Gmc3H2T/NysqpOWdy+DisbN8rfriaSVvG9/cBp1OV8HGp1EQyyd9zYZZC0lZvZsGcWEV0i6dOI+ztxs/9ld8cbeiG6Hmg6VNQLtoDL7uFQb3rR/ri52zI992exY3szy3pKlaVz4cONFkZy0NIKxtNC6+7hxQL/I8Y0JI+WUDSwa/xoHPFlOcnc+ffSdh8PfE1Up+O7gb6PzuQ9gZHK36EnKLcn7zrZRzOZE3V7bxiQnh4MIN/HjHa/x4x2tcOJiKk4cLGUfPmNKKt2/FoUsCQq/j0pOPovP2QR8UVElfHxpOzoezyPv2a4q2bsauUWPsIiIoXLmczDFPkjnmyUqD+6Wr1lFWWsY3H73N+fQLHD95qpLuOx98xkP33cmcD6Zy9nw6m7fvtppmK13hYKiyDZVT03bWddwwMk+eZ1bTUTi5GYjp2aaSVk378XKGzHocnV7P7IEv06RHa7b/sJrZQybhHuBFuNoPwNX3OXG92hLcIorMMxcq/Dbc9sJd2DvZm45T3W+Dg1n9XfrOD3w09BU2D5hE8aUc8o6fZ1PvF3Hy98QQXrnN2bkbaTbjYfQWbSBgYGfyjp+vMLj3v7UtOp2OLwZUzq9ymqh9jbmNk5uB26c9hL3ZMdre15NV037g8/4TibihOYgrD6P0eh1TJz2P0Wi4oq2GRn2hDfCtI4G3pJSJwNvAm+rfT5rZFAHLpZRdLD/AKSllqRDCWQ3paQH4Aw8CGSgD/onAKCFEK4tjfwl0VP++C/hQKFiWVeLLL7+cJYTYOmLEiF4086x0EuEdYtmzaCMAR9fvI7RttNW0clz9PXHxcefMnmPkZ2TjExlA4xuaU5RXSMbpdJLX7yHMzN6ciA6x7FZ1y+3Kysr4ZvR09PZ2lbYFNY8g69xFss9lmPkbw55Fm1Tf9qr+Vp8mZRmXTqUjy8pYNu17dPrL2RTcMop2d99En1dG4ORquKLu5q+Xkbx2DwBGL1dyLmQR3iGG9OTTFOcXknXuYoX8qmk+Sym5dCodnZ2ewPhIkNCkR2sLjdqfe3mazk5PvzdHYedgz6VTFwDQ6XU4uCkXO3npWTi6Oit50jGGQ+r+J9bvJUg9n+BOsRTnFZKblglAfkYOXpEBOLoZcA/158gvGwA4tX4vAVbyIKBjDMmqrqWNoYEnzj7upCcdq5DmFtaAHV8vB+D4+r00tKIb0iGG/b9tqmBzfN1eTu9IJrhdNIHxkZzafqRCWkDzcPZ8swKA1HV7CWxXWTeoYwxHft1UwUZnp+fGyfeTnZpOeE+lWers9LR84BbS9hwjvGcrktfvraYNxJCk5kG5nbU0nZ2e/m+O4lJqOjFqPfBtHUXILW24+ecJtBw7hOO/bQbgzLq9+Fk5niyVrH54JsXZltf9Cv4dYzisHvfk+r0EWtFoaMUmoGUUkb3aMPjHCfSa/jBCr6NBy0gCWzdi0A8TuP3zMQiDAfvmLSn8ayUARTu3YxfXvJJ+8Y5tlOzfh32z5thFx1Cyby92MU1x6JyAxzszcR07HnQV31q6Zcduet2YAED71vFs3723ku6xE6eIjY4CwNvTnZzcXKtpttIV9s5W25A5NW1nLgFe5Jy5iKObgbLSMtwDvStp1aYfD+8Yi8HDhUun0wHQ6fU4uyl9X056Fo6uBjPduvc5AC37dyH96Gmy0zJN/Xpkx6YU5xeSrZ4fVP/bUJBTuf66tYhA7+zIqXl/AXBh7V4821trA2XsemA6pWZtwN7DSJOJ91B8KRevzrGmdK/Osez7TfHh2Pp9BFsps9AOlW3KyiQ/jZ5Jodkx8jNy8IkKxOjjht7eDuSV327tYjTi6mK8op1G/VAm5d/2+TejDfCtU5PQJQl0N5u1N32A8umGAuAPlPCbqSgD99uB14BAYDFwAEAIMVYIcQi4Gxij6oQBbwFrgfYWxze+9NJLn0op23z++ecPN23YqJKD9gZHstQBdGFOPi4+7lbTymk/vAf2Tg6MmDuO9vf2pO1d3elw102kHz1DfmYuBRb25jgYHMk8dxGANoMT6f5Yf+6bPYZ7PxmDR4C3aVu5RscRN7Pxi8WVNLJUO8U3tyumCZ0OIQSFOfnkpGUqHa7Kob928fHAiRxauQODpyv+TYKr1S0nuFUjnN2NpO44gqOLM7G3tGXp5LnI0jKr53+lfBY6gRA6WgzowqWTaZxKOkpQfAQd7u15VedentZyQALnD6eSduQUrn4etLyvByc37iemXycibmpJWGJzTqxTBjT2zo7knFX8KsrOx+jrjs5eT8cn+rPmzXkmf05tOYhHWANajuhJcW4BmUfPAFCcnY+zr/U8yD2TYdUm7t4e7PtqWQX7uHt7kHXsrMmXwux8jFXkbVU2Tfp0oLSkFFlaViENIcg9o+RRUU4+zlfQLbeJHtiFi4dP4eTlyg0vD+fu1VO5c9kbOHm4kPT5EvxbRBLRMbZCXbHUzLQoG2tprdTy+uujXwmOj6TJiB6k7zzK4oGv8Wf/V5BlEtcQv2rzuzgnv8rBPYCdoWI5G6yVmXNlm3O7jvLDkNeYP/AVCrPyCLuxBZknzvPTnW/ww6BXSN24H6eetyCcnChNVwaRMi8XnUflCYZyHBNvhNISZFkZJQcPkPn041z6v8eQuTk4tK/YreUXFODn6wOA0WDgwsVLlfR6duvC+599w6q1G1m7aRsdWrewmmYrXSFEpTZ0pbytqp0VZuXh6Gak5YieZJ5Mw8HoVFnrCv1Ly8E30PWx/vxv3niGfvQk/k2CKS0qBuDYxv00u70T0d1b0ahrPEfX7THp1rbP6fv6/XR7vD+Jj/Xj/nkTaHZzO5a/9wug9Otufp7c9MQAfp/8XQX/zX8byvv/wpx8Cqqov2GjbiH36FkK1TZckp2Pg5U8Ls3Jp8RCI/TB2zj760ZOzllG4OAb8O2lXEDrDU5k16CvsbQpysmvMLgHOLJqFyHtmtBuRC+Ob9hn9Rw0NP4NaDH41jECDwshhgJBQJkQ4m7AE1iq2ngAv0sp77XcWQiRJITQSSnLhBDbgbNSykQhRHcgR0q5SQgxESiWUuapuzkDz0spf6yhjznqPgAul5/PvUxRXgH26q1gB4MjQgiraarPhHeM5ZMBEwEY+O4jzBk+ma5PD0RKaDu4K7kXs7F2HMtjbZm3Et/IQFa+r8SdvrBhlmmbo8EJB2cHXLzduHjifAWNwrxC061rB4MTQuiumCZLyrCztzdth8v+ndh2iNKiEgrzCsk8c0G5tSypUhfA2d1I74n38t3D7wLgGxnI/j+3UpCVZ7qYqG0+l5WUobe3I6BpGCkb9lFSUMS+xVvp/sxgNn65pM7nXp4W0DSMrd+toFFiC05uO0Sr2zqy7+d1hHdtTrOhiez9YQ3FeYUAFOcVYqfePrc3OoEQtHukDzvnLKUwK890TgnPDmHZC59TlJNPaJc4Inq3Iz3pGPZGJ6t5UJxbiJ3zZV2TjRAEdoph8+TvLxuraed3HjX54mB0Quiq0K3CZumEL0l4ehCR3VtwQJ1hXDrhSwKaR9CwS1PO7UjG3nBl3XIb37hQ9n67khOrduMZFUiHZweTey6DjCPKHZyDP60j4e1RZJ3NqKQHUJRXaFYPlLKxlhbYNIwt360gJy2THT+vZeDTd3Dom5WUFZUAUJCeiWuY3+UysuL/lSjJrVjO1uttZZv0AycoVf3ISD6NR5g/x1ftoqy41JSmvz0cWZCPcFTCFoSzM0JX9VxRzsx3Mdz7Pxzbd6Rw/VooVgafJSdOoA9qWMHW4OxMYaFSV/PyCygrqzw7+uB9d7J91x4+//ZH+t5yEwaDs9U0W+lKKSu1IUtq2s78YkJYNv5LTq7fS6+pDxDYPLyS1pX6l+3zVuEbGYjOTs+mL5cQ0jbaNMt+YNk2oro2p83QRHb+uJoitR+A2vc5C174lNhebdVj6SjIKzBdXDsanIjsGMv6OUsoMDs/S/8dDdbrYjlObgYcfNzJOXwKnbOyj53Rsdr6ZY5bszAOTvyaorRMzi7ciE/XZqQt3kZJbsHl8zI6Wu0TimpgA9D1yQHMG/k2AL0mDkfYOyOLq77Y1vj7+bc//Pp3oc3gWycWeEiNg5+OEq5zE/C0mU1LIKmK/dtJabpvZ34R9SwghBBtgdOAeU/YADhZCx+3oay2AxCfkZpeyeB0UgqhbRor4rGhZKSmW00DCG0XTerOI6Z97Z0d8I8O5tSeY0R2iEFKCIgNJSM1zaozqUkphLVRflQs7QpzCypsM3q5c3DVzir8jVZ9CyEjNe2KaaUlJabwkwaxIZQUFpn07pszFhdfD84dOEFEx6acP5Rara7eXs/Q955gyZS5XDql5IuTu5GWdyQyYu44vML8iUiIq3U+l5aU4ujqzMXj5wiICyMjNZ2g5hGmY9T13MvTLhw/i2ewH6eTUojp0YasU+n4xYZwescR3AK92fbJH6bjnEtKMYUL+MWGkJWaRmiXOFoM78GQeePwjQ2h5+SR2Dk74tOkIUIn0DvY4RaqxKx6x4aQbaUOpO1OoYGqa24T0D6a8zuSK9iWp6XtTjGF5fjFhJBpRfdsUmWb9g/1Jm6AUvWd3AwUZuVVSCvMzsOtoS8APlX4ez4pxRS6U26TeewcburMuV/zcLJPpZN57ByFWfkEtovGr3k4SKpsA6cqtAGlbKylXTh+Fq9g5TgNm0eQeyqdLjMexjM2BKETOPm4oXdUBoeesSHknrR+vOq4kJRiCsvxVcvZah5Y2PR892F8YhQ/Inu1IX3/CTo9M4TwHkrIUqPb2lFy9Aglhw5h37QZAHYRUZSePVtJ3/mOO3G8qRcAOhdXynJzcH1uHPqISNDpcOycQElyxboRGx1lCp85eOQoQQGVY6UBmjSK5My58wwf2r/aNFvoypLCSm3Ikpq2s6K8Qhrd0hqhEzSIjyDvYk4lrZr24xGd42g3vAdRCc3wjQqk75sjaRAbSuqOI7gH+rBu9u9WdOvW50R2jsMr2I+h744mMDaU7o/1x62BF52G9+TBuRMIjA1l0JvKA8nV/TZY0rRHG9KW7yBr11E82zUBwLVpKPk1bAN5KWdxDlXalnt8BPnq71vWrqMEt1Xyyz8mlEwrv5dnk1KuaAPg4u+JW6A3ekd7GsSF1cgvDY1/Am0G3wJ1FZsOQPmyl4LL08J2qA/ZAoOBF6xpWDwQmwV8JIT4GuVh3BPAm0A08JkQYitgD3QGHquFq78Aa1BCfW45fziV7k8PZvm0+SaDA0u2cf985cHSRonxfNz/JZBUTgOibmjOsU0HTPuueX8h/d96EPcgH5CSgCbBRHVpxnv9J+AXFUTLvp1ZPO3yrOzeJVt5eL7yEFV0Ygve6z/BtC03I5tWAxJM284ePEnK6Yvc9PRglpn5u3/JVkbOfxE31beP+r8IkmrTfKOCKCsp5Zbxd9MoMZ7ivAKT7orpP3H/3PGUlZRSVlZG22Hdq9VtfUc3ApuFkzi6H4mj+7Hp62V82HcCo+a/yJk9xwiKj+SPSV/XOp/9ogIpLSklPzOX0HZNcPH1wMXHnQ/7Tbiqcy9Pk2WSAVMexNXfg4DYMA78vI6wrs1J3XSArNMXKCm4fNFzZPE27vhhPEZ/T8ITm/Ntv4kcWLDBtH3IvHEseW42DeIj6DXtAdyCfDi7KxmPqEA6vngXwd2as+yR92j7zCC2vPWDab9ji7fR9ydFN7hbc365fSIAwV2bc8asXpmnpa7ZS++fxuPi70lEYnMWjn6PhDGDWDP1su6hJdu4e/5lm6/6TQQh6Pf+Y8QPTSTtUCopq5M4vTPZlHYh5QwNW0TQ5cW7CElszuJH36P9M4PYZObv0cXbGPij4m9IYnN+6DsRWSbpPnUUjW7vgN5Ozx8PzaAoO5+b3o2h3f8NoCgnn/y8Qg6s3IFfVBDxfTux1Kwe7FuylQfVsmmcGM/7anlZpskyyaApD9K8T0f09nZsGjUDR3cjCbMeASFIXbGLwBviaPPSXQR2a86aR96jxbOD2DnlB2rKyT+3cdPPyvmFJTbnj9Hv0XHMIDZMrZgHg364bDOv30TS95/k5pmKH0eXbufk2r1cOHSKPp88SadnB3N2+xEKlvyJcHTE4+2Z6Lx9cGjXnqzXXsZw3/3kffGpSb/gt19xG/8yTrfcRumxFIq3bqEsPR235yeAEBRuWEfxjm0V/O5+Q0eGP/IMaekXWbNxK2+9PJYZH3/J4w9UvFH62bc/MHzoAJydnKpNs4WuLMolZkBnUxv6bfR7dB4ziHVmeVvTdtawQwwDv3qG5sNupKy4hO1jP6lzP560cD0Aji7OjNk0i8KcfOJua8/+pdtI+nUDXR/te9X9bXna7oXrES5OPDz/Jewc7LBzcuC9/hNw8/eiZd/OAPww9hOg+t8GSxrf0JyMmb+Sm3KO9gsn4tTAE58bW7Drwek0GjuEw29+X+W+ACmzfiXu7QeIfLI/pfmF7BihzLSf+2MrzX9VHhKOSoznp9GzSBwzmFVTK+bzfWqeRiXG81m/l6we4693fmT4vPEYvFw5vHwHAeH2Vu2s8cWsKTW21ag7//bY+L8LIbWMqIAQ4kZgiJTyIfX7XUAxsBllUP0E4AaMlFL2vYKWE8oDtYeAb6WUyWbb/IE7pJQzhBAvADop5au1dNcT6AGsfjHsrjPWDJzcDEQmNOP45gPkqA8+WUurigLKcHYz0iihGUc376/Wvjo7y20OWL/96eRmJCohjmMV/K0+raSwpNL2uupaw+BmvGJ+XSmfSwqLK20vs7iNWJdzt/THyc1IfBdlcJ9Xha+O7gZCE5pVa2OJl6uBhgnNOLPpAPlV7OPgfmUbS/I8DYQnNOPk5gOmBw8r+et2ZRtLvF0NBCc04/QV8uFKNpa227buq7auOLkZaZQQR4pFeVmmmRNTXPlGqoO7gYAbmnFu4wEKanjOluR5GQhJaMapK+TBlWwsGRat3GgULi44tGpLUdIuZMbFOvlojsd3nwPKkoIbtuygTYs4fLy9rlq3nPrU/ajbm1dsQzVtZ+Z2Zy5csmpT237ccptlX3PZru59ThGyXn4bLLkxX/HVzt2IT9dmXNywn6I6tgFzkvxciVDzpKp+xMnNcEUbS8Zus75M7dVg7xNR75o2pvZxhDYk0qfV3zawTU7f/q86d3O0Ab4VhBAOUsqiara7Ay5SysprrdXteB5AlllYT615MewumxRkAXV2qVqqGuD/G9HZyNeqfnSvFs+y+o+8c7FNNSBDf2WbumArf0/p61/Y2gC/PrBV3pYP8Oub8gH+tcCMVtbXq79aMnW26RNs0dcU2aj/Kh/g1zdbnGzTILQBPvAvG+BH+LT82wa2R9N3/KvO3RwtRMcK1Q3u1e2ZwNVPKVzWu1RfWhoaGhoaGhoaGtc32gBfQ0NDQ0NDQ0PjP8FVBEP8p9BW0dHQ0NDQ0NDQ0ND4D6HN4GtoaGhoaGhoaPwnsNXzbdca2gBfo1rsbfTsjC2an62edLnWOosMXf3fnsysw8uWaoKNnimk6Bq6N3nE3jaZUGqjejv3QLBNdB+yiaptuGiDNgZgL23Tztxl/TeIdBvlwWana6jx2oji9KM20b0GH97VuAq0Ab6GhoaGhoaGhsZ/Am11SAXtUllDQ0NDQ0NDQ0PjP4Q2g6+hoaGhoaGhofGf4FoLq7UV2gy+hoaGhoaGhoaGxn8IbYCvoaGhoaGhoaGh8R/iugrREUI4Synz/2k/NDQ0NDQ0NDQ06h/tIVuF620G/2shRDchxHghxHIhxDIhxGr1/zVCiFAhhJsQopcQ4nUhhLsQwiCEeEII0dJSTAjRRwjR0ez7t0IIRwubV1Q9eyHEDjUtUwixSghxTAhxu5o2SQiRKIR4TQgxVgjhKoRYLITQ2zpTAJzdjUR2icPg6fJ3HE6jHrhcZq7/tCsa/0Kuhfrh6GEkOCEOp+u437kWykmjIi5+HoR3boqD0emqtTKzslm/eTsZlzLrwTMNjctcVzP4wHjgIynlDcCrQoiBQDcp5WgAIUQTYATQAIgE/g9YBQwFdgshugCnpZTli9RuAD4B+gshOgMZUsrC8oMJIboD9wF9gEtAlBBiFHBQSpkohJgIFAkhXIAsoBPgB/gDYUCulLJUCKEDkJXfv/wpEAP8XtUJ9508Ct+oQA6v3MVfs36xmubi68GdHz3JweU7uHn83Xw+7DXyLmbTd/IoGndrQfK6Pcz/v/erzNT+k0fhGxXEoZU7WaUew1oawODpjxLbozV/vb+QXb+so8+k+3B0cSZ1VzJ/vPZNrTQdXZ15YskUHIxOZJ/L4L1bn6e0uBSAPq+M4NCqXRxcvr1WPpan6R3sKC0qIXn9XoKahaPT6ynKK2De6BmUFpdi9HFj9B9vknHi/BV9vGPmY6b9C7LyCGvfBCEEl06l4+RmIHVnMgte+LRW5+7i68HoP96gpLAYvYMdM3s9R97FbACMPm7c++VY3r/thVqft6XfDZqEYOfowMYv/mTV9J8x+rgx9P0n+HTIK7WqY+UYfdwY/uVzfHDbOAAGTH+Ext1bsv6j31g987KdObdPuaxVbmP0cWPIB0/w+eDLfvhEBXLTc0OZO+pt+kwZhU9UIEdW7mJNFbrWbIw+bgz+4Am+UHVd/T25f8EkhF5g7+zIxjlLWfzWPKt6AAMmj8IvKoiDK3eyUj1va2kA/7fsLQCSflnPpjlLGDT9URyNTpw/fIpfx312VXk7aPqjRHdvyckdR3DxdedTtU1bUtP6YfRxY9j7T/LJkEnc+NZIvKICObfrKG7Bvvx2/zuVdMttjq3cxdYZC0zpXV+7j+Mrd3Fs2Q4Mfh7c+vETHFu+gy4v3gWiFMy6uAlvvMPRYye5oWNbHrzvzkrHSD19ltfefp/c3DyaxUbzzGOjqiwXc65WV+/iwwM/TqzUt1lSk7x1tWjHH/QcS1lJKQNnPIqD0Ym0Q6dYNO4zq23AMs3JzVBpP0dXZx5ZOhlHoxM5Zy/x1S0vUKb2keb0nDIS76hAjq7cxaaZl8vL4OPGwDnP8tWt43F0N3Dr9EdwcHHG2cuVnMycSnXakpq0Byc3I0OnK36fP5yKg50dftENcXI3kpmaTlFuAT+MnlnB79q03YvHzgKQfS4Dj2Dfq+4T/JoEc8uk+0hZt5ebXriT8re61KVepaVf5MkXXuGGzu14a+YnfDrjDbw8ParMT2ukX8zgqXGvMeeDqbXa779MmTaDD1xnM/hSyv1Ad1DCdYDXgGVCCB/V5DBwA9AUCAS6AK7ACSAIGAX0UPdvCOwBPIUQq1StpkKIS0IIe/V4y4GPgCellInAXinlJ4DlQN0d8AaeB5oAF4BHUS4IVgOpQBuLfQYAepSLgkCvMP9K5xvTqw06vY7ZA1/G1d8DrzB/q2l+jYP445WvWf3eAo6s3k1gXBgxvdrg6u/Bie2HKMzJxzusgdU8je3VFp1ex8cDJ+Lm74l3WAOraeW2wS2iyDxzETd/T/q8MoKVM3/mkyGTcAvwIrxDTK00b3l+GOcPn+LV5qOQZZJWg7sCENo2Ghdfd9PgPrZXW4S6r6uZXlVpaz/+DQeDEz8+/SFh7ZqQ9OsGvrjnDbLTMmnUNR4nNyP3fjkWvZ3+iprxfTuzbvbvfHHPG9g5OeAe6M3bN/wfh1bt4pexszm2+SBbvl1RIT9r4mu7Yd05tfsoUzs/Ts75S0R3b2XSuOWFu7B3cqj1eVv6nbozmSNrk0j6bSMNW0QREBfGgGkPYW+4fJOqpnWsnF4v3IWdkwMA8f07E9Yhhozj5yrZmfRvboNOp+PTAZe1nNwM9J/2EA5mfniG+NHzhWE4uToTc3MbhE7H5wNexqUK3SZWbJzcDPS1OL+glpEcXrGD5L+SmBI3Cic3Q5VtoalaRz80q6PW0gC6PdoXe2cH3rnpGVz9Peg48lZ2/byOT4e8goPRicBm4XXO25hebTB4uvD5PW9w4dhZTu5MJjAuvJK/NW1nTm5GBk17GAeDo2n7kic/JKJnawy+7pV0I29WfPyh/ySM/p64qz4GtovG6OvOsWU7APBuHMSal79m68yFnPhrN8Lucr4vXbWOstIyvvnobc6nX+D4yVOVjvPOB5/x0H13MueDqZw9n87m7butlos5V6srHAyAqNS31TVv26rt+K3Oj5N9/hKNu7ckfkACu39ex+eDX8HBxYlOD9xWqQ1YaxeW+wU2C6fnC8NIO3yK9+IeBFlG3JCulXyNurkNQq/juwGTcPH3xMOsTnUdP8zUXmMHdGH/z+vYNvsP9PZ6fp34ZbV5UNP20GpAF3b8vJaPh0zCLyoIo7cbu35YQ/qRM/w+/nNy0jKJ6hpv0q1N210z6xfmDH2NzV8sprS4tF76BN9GQSwc8xGrp/9Exok00NvXuV4dSTnOs48/wIP33kmn9q3YfyjZal5WRWZWNuNenUZ+QUGt9tO4PrhuBvhCiGHqQHyEEEIA7wPl8fhzhRAJUspSYDrKYP1pYAbQFngTOARs4fJseRHwp5Qy0fwD7ARKLA7/rnrsciLV7/ep30tRZuynAadQ7iC0B8YBDwLfSyk3W2gmAt+rf68IbRtd6ZzDO8SyZ9FGAI6u30do22iraUfX7SV1xxFC2zUhKD6Sk9uPENGpKf7RwWSkplOYk481feUYMSQt2gRA8vq96jEqpwG06N+Z9KOnyU67RPL6vfg3bsjpPSkA5KZn4eRqqJVmcWEx6z/7E4DC3ALcArzR2enp9+YoLqWm06RHa5PeHnXfo2Z6VaWFd4hh7x+bCW0bzcYvFiN0SjMxermScyELWVZG6s4jZJ3LuKLm5q+Xkbx2DwDeof4cXLHDtD26e0tcfNxNeVAbX509jGyas5Swdk3QO9hh72gPQETHWIryC8lJu1Tr87b029ndyJ5FmzB6uXJ8y0Eatoji+9EzKczJN/O3ZnUMILxjLMWqbwAhbaL58+WvKMzOJ2X9PkKs1LGwDrHs/U3RKreRZZL5o2dSmH3Zj6LcAuY99K5pn33qPsfW7yPYim6oFZuyMsmPFroNWzYi5pZ2NGwZSc8Jd5O8fi9hVbSFCIs6GtY22mqazk5Pl1G3cTrpGDE9WnN0/T5cfNzwiQzAyc2Ae4A3macv1DlvwzvEsuGzPzm54wjZ5y8R1jaak9sPV/K3pu1MlpUxd/QMCnLyCe8Qw+FfN1GcU8D6N+fhoLZZc4I6KjYAqev2EthOOecbJ99PVmo64T2Vi9GTa/dybkcyge2j8W8RiSy5PEjZsmM3vW5MAKB963i2795b6TjHTpwiNjoKAG9Pd3Jyc62WizlXqyvsnSkrzKmQP9aoad46exjZaNaO7RwdyLuUjXfE5brgHRFQqQ1YaxeW+2WevkBJYTGb1D6yKLcA1wDPSr4Gd4zhkOrXifV7CVLPKbhTLMV5heSmKaEj+Rk5eEUGENa1GSX5RVw6faFe2kNeRg6+at33CvUnacF6tn61jN0/rSG4bTQGbzdyL2SZdGvTdtvc04P//TyRhMf711ufsPfXjVw6lU6jG1vg7G6A0uI616uObVsSHxfD1p1J7Nl3iPi4Jlbzsir0eh1TJz2P0Vi5HV7PyL/x37+Z62aAL6X8FpgIRANzgAPAl+rmu1AG4ZGAI0rokh3gLKV8EWV2fZCUcpaU8qS6jw5oo8bSmz4og3PLuPmlwNdm39NQ3sy+SP1uB7wM2KMM8t8BzgCtgRDA2nurjXfffXcHIcTWAQMGvJ7panlNAfYGR9MgtDAnHxcfd6tpAH1e/x+DZzyKf5Ng7vpsDO3v7Un60TOs+ehX3AO9ie1peQNBwcHgSNa5i2Z6bqa0vq/fT7fH+9PtsX6MnDeBpje346/3Fphs05JPc+MTA2nSvRWNusaTvG5PtZqth3Tl/rnjTZqdRtxM1rmLBLdqhJ2jPcV5BbQckMD5w6ms+ehXGsZH0uHentX6aC3NweBI5pkLuPi4mbYFt2qEs7uR1B1HKMzJR2+np7So+Iqa5QS3aoS9syNH1+81bW9yU2s2f7OsxvlpLS2udwdKikoweLqit9fT7YkBLJk8t05aln47GBxx9nLB2d3I6aQUnFydK/zQ1aaO6e31JD7Rn6WqbwA6Oz0XUpTb54XZl+tiJf2zGRVsCnPyK/mReyGL0qIS0z7ZFvtY4mDFpkjV9Y4IYPjccQyfO46IG5ph8HLlx9Gz8I5ogMHTpUK5WvqaaZGP1tJaDUigMCef9XMWExwfSXjHWAoy8/AOa0CH+3qRnnya/MzcGudt3zfvZ+hHT+LXuCENYkPpMKKXySaoeQQ6vY6yUsubhjWvH+b57WBwJPdsBvkXsii4lIPevnKUp51qA1CUk4/Bx50mA7tw8fAptn+wCP8WkTS/r4fJvlGfDkr4hdlvZX5BAX6+yo1Vo8HAhYuXKh2nZ7cuvP/ZN6xau5G1m7bRoXULq+ViTm11W3fqSvdb+qF3D1A+zu7IstIK+WON2ra9Zr07UFpUgsHLlRNbDuEd3oD2al3Q6XWV2oC1dmG5n6kOnc0goFUUekd7ivOKKvlq7+xITnl5Zedj9HVHZ6+n4xP9WfPmPDwjGjBk3jha3teD5nd3J/6u7lw8eob8zNxq86Cm7eHY1oN4hzWg0329KMot4MLRM6bzCoqPwNndwKkdRyrkbVVt15wjq3bx+YCJfNZ/IgZPV+ydHSvsY63MaqIL4GB0Iva29uRfUi7+rqa+Sin5c/lq7Oz06HS1e+TOxWjE1cVYq300rh+umwG+GcXAl1LKyeUJUspzwDCgGfAUyoXAW8D/CSH6oITrhFjoFKKEzQwEvgX6qTP4vVBDcIQQjwF3Au2A3kC4EOJRIBvwAcovu0OBD4B7gZdQBv59UQb4HYFNVs4j5+uvv/5dStnmp59+GtvQyaeSQVFeAfbq7VUHgyNCCKtpAL++8BlTOzzGpi+XsHnOUs7uO87B5TvIScvkxPbDeIX6Wc3MorxCMz0nhNCZ0ha88Ckr3v2JrXNXkbxuDwdX7jANNBwMTqRs3M+hVbtoMzSRHT+upiivsFrNtZ/8xqdDXzVprv/8T1y83ekz8V42f7MMIXQENA1j63cryEnLZNfPawnvGEthXqHpNnO5XnVphXmFOLkaEEKHg8EJe2cnek+8l5+f/fhy4ecVmmb2q9ME5SG63hPv5fBfu0zbHY1OeIX4kbJhX4X8rK2vi178ggvHzuHXKIgbHr6djXOWUpCVVyctS79LS0rp+khffn724wp1pS51LOHh29ls5lulfY2OCJ0V/dwr21jbx64GulXZXDh6hjlDX2PO0Nf4tO+LbPrsT+ycHEhPPoNXiL8pfyrnRdVtwTwtsGkYZ/Ydpzi/iB0/ryWwWThRXZvz67jPWDXjZ9KTT9Ny8A01ztvM0xdZOHY2nw6exNl9x9nw+Z8mm+0/rCbz9AWadK+0RkCN/bXcR+9kb9qOleIozq1oI3QC37hQ9ny7kry0TA7+tI6GnWJN9n+N/5Iz2w6r4S8KBmdnCguV/iAvv4CyssoXKA/edycJHdrw46+L6XvLTRgMzlbLxZza6h47sBuZc47SzDPKJz/T1A6s5Y95PtUkb8vTflXbsW+jILo/O4RFL3zGX2pdcA/yrtQGrLULy/1aDr6BotwCXHzcuHHScHZ9tRystOHivELs1PKyNzqBELR7pA875yylMCuPjKNn+f6O18g8fp7PbhjD9s8Wk3PmIq0Hd62XPOj1zB38Mu4zVsz4mZz0LGJvbQeA0dedxje1ZuEzn1TUrUH7Bji57RBFucpdobyLWXiqYTl16RMsKczKY8HTH1FSWIywc7yq+iqEYPzTj9IiLpa/1ln7qdeoLVLKv+3zb+Z6G+DbAUgpy6dN7c3SDkopfwHWo8ymzwN+lVL+CtwC+AkhzAP35qLE6D8FJAPvquk3Ax+rmjOBmcDjwFSUGP7TwAop5VqU2H6klOtQwm2Wo8z0fyOlLAa2owz0t1g5l23q8QHiM1LTKxmcTkohtE1jABrEhpKRmm41rctDvYkfoEg5uRkoyMrl3KFUQtsqtwsjOjXl0qkLVjP0VFIKoW2U250BsSFkpKZZTYvsHIdnsB+D332UgNhQuj3Wj4zUNM7sO4Z7oA/rZv9ea80z+47R55X7WDxlLm7+nmSkpnHh+Fk8g5WLkaDmEVw6VX7O0eo5K/tWl3Y6KYWIzk3JSE0jMC6M2F5tWDJlLpdOXc7j00kpOKrhCdVp6u31DH3vCZZMmUvKxv2m7dE3teL84dQqyuzKvrr6utNpxM0AeAR5k3EqncjOcXQY3oP7546nQWwoATEhtTpvS79D2zbh+OYDXDqVbqorda1jEZ3jaDe8ByPmjqNBbCh93xzJ6aQUQtoqdv4xoVyyon+mBjZ12aemunfNeY6Lx88R1jGWyBua4eDsSEZqmlXbU0kphFmpt5ZpF46fVR62bhNNw+YRICWlJSX4RwcjdIKgllEg6563DWJC6PpYP1oMSCAgNoTS4lLyzS6szP2tSTuz3CewnbLdJzaEksLiSrppFjZZqWlcOnYO9xClXfo1DyfrVDqtHu5Nk4FKv+PoZqjwgG1sdJQpzOHgkaMEBVSOmQZo0iiSM+fOM3xof6vbLblaXVlSiLBXVk6xlj/l1DRvXXzd6WjWji+lpmHv5IBfE7UutIjiUmp6pbpqrf5a7iclnNt3nFtfuY+1k7/HtYEnWVb8PZeUYgrL8VPLK7RLHC2G92DIvHH4xobQc/JI7Jwd8WnSkHN7UmjYIQakvGIe1KQ92Ds70ECt+3oHPZ5hDdDZ60n8v4Hs/mkNmacqts3atF0XPw/snBwwervh7Gasdp+a6t766ghC2im/jU5uBqQsq3O9+vTr71nwhzIcyc7Jwc31+l1NSqP+uW5W0VGXm3wJaC+EyAA+RImxLzOzuR0woszOFwO+Qoj/oVwIPAXMEUL0BzxUm0tAiZRyuRBiixAiDCX+/owQopVqVz7gtwNmo9wlGCqEMALBwDr18B8CuSgr/RwXQoSjPOxbBLQCtlqc0i/AGpS7C7ecP5xK96cHs3zafJPBgSXbuH/+BFz9PWmUGM/H/V8CSaU0IQR3vPc4re/oxvlDJzmyOomzB07y2LIpPLthJgZPV2bd+jy+UUHE9+3EMrNj7F+ylVHzX8TV35PGifF82P9FkFRK271wPY4uzoya/6ISa+rkwMGVO+j1/J1cOpVOcUFRrTVbD0nEK8SPAW89iMHDhV8nfM7xrYcYNO1hmvfpiM7eju8efpei3AJGzn8RN/WcP1L1qko7vvkgYe1iuJBylqY3t8Pe2ZH+b44iIzWNTV8vY8+ijexfspU+r4zglvF3V6vZ+o5uBDYLJ3F0P4Reh3uAN27+nkTf2Iq/3vuFm54eXCk/a+LrsS0HeWLpFJ7ZMBMHgyMLnp+NLCk1ad0/dzxfj5xWq/O29NvN35PmfTvTuFsLhBB8NeItuj89uEIlrGkdS1q43rTPiLnjWDB2No4uztz//QScPVww+rrzw+hZ3DhmMCumVqzDI1StqMR4Zvd7iSthvk9kYjw/jZ5FtzGDWWmhe5+ZzWdV6K6e/jO3vfY/3IK8Sd1+mMjOTVk8dR5+altYalZ2+5Zs5UE1HxsnxvO+mreWabJMMuTtR+j+5EAKc/Ipzitkxbs/cufH/4edkwOp2w+TtHA9QqerU95+O/JtRv74Ej2eGozB25XdC9aTefqC1bpWk3Zmzv4lW+n2/YsY/T0JTWxOcV4hHZ4ZxMa3fjDZJC/exqAfx5ts5vediCyTdJ86ika3d0Bnp+ePh2ZQkl/Ize8/RuydiVw4mIosvhwK0f2Gjgx/5BnS0i+yZuNW3np5LDM+/pLHH7i3gj+fffsDw4cOwNmpZssVXq2uLMpF7x7ILePvNuXP1fSLx7Yc5MmlSj/rYHBk0dhP6TCiF33euB+PIB9Obj/Mny/P4e45z1VsA5JK7eLisbP0nfqgab89C9fT6s5ueIb40eutUTh5uLD+3Z/oPGYQ66ZeLq8ji7dxxw9KeYUnNufbfhM5sGCDafuQeeNY8txsGsRH0GvaA7gF+YCUNGgSQlSXON7v/+JVtYcLx88x6C3F79RdyfhEBjLsi2dxD/IhvEscj/ftxLI35rJPffakNm13+HfjKC0uYdNnf9Ksfxf09vqr7hPWf7SIfu88DFKSvCaJ8BYeda5Xg26/hacnvMFPvy4mKiKUTu1aWTukRi0p+5fHxv9diH/7LYb6Qh2YD0UZ5D8AdEUZjDsCLijLU05DWXryZ2AsygD6N2CYlDJNCDFc3ecvlLCaU6pNeYx8IPCKlPIr9Zg+KBcAl9Tv4UAvKeWHQggP4FWUlXPsUEJ0jgKvA7HAFGAMcA74QfXB8hF7T5RVfVa/GHbXGWvn7eRmIDKhGcc3HyBHfVjKWlpV+4YnxHHsinZGoizsrKVVl15XTfO03HrUO7P/BAExIdX6WZvzrs351+XcbaVVnn5i88FqjlH3OmZwvbJdTbUq6LoZiFD3qapeONXAxtL24Jb9V6y3jRLiSLHIW8s08/RUG+StLdpuOaEuLgQnNOP0pgPkVaHt6G64oo0lD+2YVOF7ZlY2G7bsoE2LOHy8vWqkUROuWlfo+OHFBTbpwwrPZ1WhdXX9eNOE5qReobxCE5pVa2Np73VD00p12loe1LQ9lOPpaqzXtlubfeqi+/w2ZflMW9XX+sTeJ8JW0leOnfwb8XWP/tsGtmmZB/9V527OdTPABxBCOJqvU2+WrgeklXXm/zaEEHZSyhL1bwHo1FV9EEIIeYWCejHsLpsUZOk1dCX8r21l/wF0NspdnY2ql61iD/NF/Ttsb6O8tVXbbVBqm9y1HOD/m5nYZrxNdO2lbeqCqw1003W2+bl0tlEe2IryAf61wPUywPdxa/y3DVzSsw79q87dnOsmRAfA2uBeTa/85o+/mfLBvfq3RFk60/y7hoaGhoaGhoaGxhW5rgb4GhoaGhoaGhoa/120N9kqXG+r6GhoaGhoaGhoaGj8p9Fm8DU0NDQ0NDQ0NP4TaFHNCtoMvoaGhoaGhoaGhsZ/iOtqFZ3/MveGDbRJQTYru/LbIetCng1WI7HFCie2pHYvJa85aVR++dDV0rjMsd41AW4k2ya69na2eW7+w7L6fy38TQW2qQm2WhLsvL1t5oVy/rVrUVTGVq7m2qgPK7KB7tqS8/WuCXAXDWyiu1B3ySa6nYVHvWva4vcRYMJW26z4Y+8T8a9qvZ4uUX/bYCAj58i/6tzN0UJ0NDQ0NDQ0NDQ0/hNoL7pS0EJ0NDQ0NDQ0NDQ0NP5DaDP4GhoaGhoaGhoa/wm00HMFbQZfQ0NDQ0NDQ0ND4z+ENoOvoaGhoaGhoaHxn0B70ZWCNoNfBUIIRyFEZC33qfOSM0IIbyHEnerf9kKIf+2T2RoaGhoaGhoaGv9etAF+1QwDZla1UQjRUAjRQwjxoFnyp0KImy3sMoQQy6r4rBRChKumOcCbQojmwBxguZldhhA2WItLQ+MaR+/ugmtCPHpP13/alf8cTn4e+CbEYWd0+qdd0dCoE44eRoIS4nD0dKk3TU8/T+K7xONsrPsS0k7uRsK6xOFcj37VJ+kXMxj+8Jh/2o06I//Gf/9mtBAdKwghfIFXgWQhxCIgADiDckHkLKXsBrwNfAA8LoQIAV4DnIG7hRBLpJTly1AflFLeJIRwlFIWmh2js5Rynfq3HvACngLOSinvtPBnFVBILfjf5EcIjApi98rtLJz1Y43tDG5GHpr+JE5GJ04dPomv3hGvqEBSVu5i48wFVjV6TRlp1cbg48bAOc/y1a3jK6SNWD6Z9KNnOLJyF2tm/mJVs8+UUfhEBVawMfq4MfiDJ/hisLKWb9f/G0ho+yYANGgaSn52Plu+W8HKWdY1AQZMHoVfVBAHV+402Vmm6fQ6nln9LhdPnscnIoCc9CwOrtiBT3gARm9XLp26wI/PfUxZSWmNdT2DfU1/u/l7cnDVLg4v3067u2+iWe8OAPhHB1NWUsrGL5ewStXoP3kUvlFBHFq5s8o0Jzcjg6c/iqPRifOHU/l03EfcM/lhAqKCOLR5H01vaMFrtz1rNT/K7fas3M7vs35Cp9fx2ur3SDt5DoC5L33G6YMn6DllJCFdmnLhYCo/j5hmVavnlJF4RwVydOUuNlVRDxzdDdw6/REcXJy5cCiVshdmABAyZTSOUcFkrdzKuZnzK2nrXA2EzxoDdnrKcgs49uhb6D1difhoLJnLtxI04X8cGTqBkotZFfYLevNxHCODyV61lbT35lnVDZ7+LELVPfn4ZGRZGY3/mk3xCSUPTr/8IYUHj5v2GTb5QRpEBbF35U4Wz/rJal5UZ+fq484jXz7P5NvGotPr6Ll1BnknlDXFizJycfRz59zynRx69xeruo4+brSd/SRr+02qkO7apCHNJt7N+qFvVkhv9fYoXBsFcXb5Tg5Wo9l+9pOsVjXdYoJp8fp9nF+7l2YT7mTlbS8ii5X63vWtkXhGBXJi5S62z7DeJ1jaxN7Tnag+Sj13cDNwfkcyq5//DABnHzdu++pZvlD7iar6E3Nq0+dcaZ+66FZV182pSXsQeh0j175N5ok0AHIyc3Dxdb/qvtHV35NHV02jrLSU/IwcPug3gbyLld85UZP+xdHVmTtmPoZOr6cor4B5o2cgyyTz1n3LmRNnAJg+YRaDRg4gNCqETSs289WMb6z6DuDp48HLH73E4wP/D4D7nh5Oiw7xAHj5euKcW0JZcQknV+xiRxX164apI/GICjTZOLgb6DbzERyMzmQcSmXt85/j7OdBj0+e4MSyHXR46S4WD32OLLV/eGzK4wRHBbN15Va+n1m5XyjHw8eDsR8+z9hBzwEQ1iSMB195iF3rdjHi+RGk7D9Gk8gQjq7cxfoq6sEtZvVg/cwFGP08GPDRExxZvoMbJ9zFd3e+TllxCbfPGo1fk2DsHB3Y8MWfrJr+s1W9vpNH4RsVyOGVu/hLLTNraQC9X7mPw6t2cXD5Djwa+qJ3awBCIEsKKcu9aFU/Myubca9OI7+goMp80bg20GbwLRBC2APfADOllDdIKXsDxVLK3lLKW6WU3dQLAB3gCXyMMth/Uv37M2CmqmPOd0KIF4RCG+ATIYSXus0F+EFK+SPwlcUsf/lFWI0vFVv3ao9Or+PVgePw8PfCPyygxnadB3Rl/c9/8fqQCQRGNcTg48Z3Aybh4u+JR5h/JY1GN7dB6HVWbbqOH4adk0MF+97vP4bQ6/l8wMu4+HvgZUWzyc1tEDpdBRsnNwN9pz2EveHyC5f+eudH5gx9jc1fLKYgM48PB7yEm78n3mHWX5TStFdbdHodHw6caLKzltagSQi7fl3P+s8Xc3j1bmbe+jyNu7Xg2LaDfDL0VbLTMmjRr3ONdUPbROPsbuTDgRMJahaOV6gfB5ZvB2Dz18v4dOirbPh8MTkXspgz4i1cVY3YXm0Reh0fD5xYbVqLAV3Y9fNaZg+ZhKPRmZ4P9EGn1zFl4Hha9mqPs6v1maaWvdqZ7Nz9vfALa0BQk1C2/LqWt4dO5O2hEzl98AQte7XD0d1IzpmLZJ2+YLUeRNWwHsQO6ML+n9cxb9ArOBidcG4ehfvNHUCv4/CA57D398LRSn316teV87MXkHzXSxSnZeCa2ArnxiGkTvqUc7Pmk/XXDpzjKkbUufXqCDodRwc/g52/Fw5hgZV0Pfomkv7ZLxwbPoHi9AxcbmiFU5NwMn9dTcqw50kZ9nyFwX28mmdvD3wRd39PfKuoa9XZ9X/hbuzV/AhsEkrqL+tZO+BVkmcvpiQnjzV9JuLUwBNjeGVte3cjrWY8jJ2h8ovHmk28G51DxTmbwFuV+vJXn4k4V6PZxkLTrXFDtj35EQem/UTuifMYQ/xMejq9jl/6T8Lo74m7lboQfnObSjb7vlrOwiGvsXDIa5zZfJB9364w2XccPwy9mh/V9Sfl1LbPqW/dmmjVtD34xoRwYOEGvr/jNbZ/vpjC7Px66Rvb/a8X5w6cYErcKI78tQtnt8ovaKtp/xLftzPrZv/OF/e8QXZaJo26xuPfJITlC1bw5OCneXLw0zQMD0Kv0zG63xN4+3sTFB5U6XgALu4uPP/uczgZLt8V+mLaHJNO1qVssk6ksbDfJAz+nriFV86DsFuUPDC3aTSwC0d+WsevA1/B3sUJn+bheDYOYuPLX7Nz5kJSV+0mMi4KgI43d0Sv1/HsgGfw9vciwEq/AGB0N/Lk2/+Hk/PlfA1uFML0Me8y993vKCoqxuhm4Gu1jD2tlFfjm9ug0+kq2Pg0CmL5pK/ZMGshKat30yAujNh+nTmzI5lja/awf9FGGraIslr+Mb2UtjV74Mu4quVvLQ0gtG00Lr4eHFy+A4CeY4dSlpdBaeYZhM4OYW/9zpxer2PqpOcxGg1Wt18LlEn5t33+zWgD/MoEARuBBkKIReoMfrQQ4nchxBIhxGggDlgNtAUMwP1AU6Ah4AGsBO6w0B2KcidgLsqFwBApZfkldCGYXj9qJ6W8SUp5k/p3SW1PoEmHpmxetB6AfeuTaNy2SY3tcjKyCYgMwuBmwD+0AfsXbADgxPq9NGwbXUkjuGMMBxdtqmQT3CmW4rxC8tIyL9t2isXZw4XsU+kAHFu/j2ArmqEdYtn328YKNmVlkh9Hz6QwO7+SfdPeHTi77zhZ5zJIXr+XMCuaABEdYkhSfS23s5YW0jKKpj3bcvuk+/AI8kWn11FWUoq9o3LNlpOehZOroca6UpZx6VQ6Ojs9DeMjQQpierSu4FvjG1uSdzGb03tSOLp+L6FtownvEMMeVaO6tPyMHHwiA3ByM+Ae4IVfRCDbFq0numMcF1LTKCm2/lbXxh2ask0t/4Pr9xDVtgkRLRvRomc7npn/Cv9793F0eh2NOzTFPdSPjKNnKSsupWF76/XgkFk9CLKoB7lqPcjPyMErMgBHNwOugd4Un0rDpWMzLi1ap+Tt+iSMbWMr6ad/9QfZa3YBYOftTkn6JbLX7iJvxyGM7WIxtmhM7vYDFfYxtm9G5u9rAcjdsBtDm8q6F7/+ndy1OxVdL3dKLmRiaBmNW8+OhH8/mYbvjAH95W6yUYdYti9S2sSh9XuIrKJtVWXXuGNTivILyU67BEB4y0YE3tKWhAUvEfvCHZxatFk537V78baSz7K0jC0PzqAkp2I7CLmzK2nr9lWy9+kUQ+pCpVzOr92LTxWamx6cQbGZZuqCDeSlptPgphbYuxvJSTlr0kv+VdE7tW4vDdpV1gvsWLWNsYEnBh930pOOKbZq/chX60dV/Yk5telzrrRPXXSrquuWWjVpDwEto4jq1YahP04g4bkh7P9DKf+r7RsjOsfh4uvB/36eiHuAD6FWtGrav2z+ehnJa/cAYPRyJedCFsEto0i4uQszf3qXcTOfp2WnFqxc9BcAO9bvoFnbuErHAygrLePlh18lLyev0rbo+GgcnR05PH81AKfX76WBFb8DOsZwVPWx3KYgIwf3yAAc3AwYA7zJOXWB02v3cn57Mg3aR+PbIpID2/cD0KxjM9YsUvqFXet3E2ulvyn3dcqjk8kzaxdrfl3N+dTztLmxDT4BPiyduxSA41XUqZAOMez/bVMFm+Pr9nJ6RzLB7aIJjI/k1PYj7PhqGU4eRvb/tgmDtxvHtxysosxi2bNIKf+j6/epZVY5TWen5/Y3R3IpNY0m6m+Nd0QAskQJBJBlpSCsD/9cjEZcXer/jd0afz/aAN8CKeUxKeWLQHOgnzqDf1Cdve8ppZwFHAf8AAdgERAGfAFcAMKBTCnl1xa6RVLKx9TtrighP9ZoUj57D8RX56sQ4gEhxNZvv/027Ymvn2fs3JcZO/dleo64jYxzFwAoyMnHzcfD6v6OBqdKdoe2HsA/LIAe991GQW4BF5MVNwuz8zH4ulfSsHd2JOdsRgUbnb2eTk/0Z82bl299lqed33ec0qISk72LT2VNB4Mj2WaaLj7uFOXkm37Abnv9fwyfO870adqnIzu+W6nY5+Tj4uNm9XztDY5knrtYwc48rfXgrtz4WD/a3NGN3Ixs3Pw9yUm/RHS3FhzdsI+4W9oT1SWONncksm/p1hrr6nQ6hBC0GpBAxsnzpCYlExwfSYd7e5o0gpqFs+PH1RU0HAyOZFnoWktr3K0F7e66iUd/ex2vsAYkDL2JrAuZ3PbEIFZ/swQ7e+uReA4GJy6pWvk5ebj6eHBsdzJT73iJtwZPID8rl7hurfALC0AA66b9iETSdNANlfPWrB4UZedjVOtBR4t6cGrLQTzCGtByRE8uJp+mJDMHvbMjxWeVelianYe9r4dVfwEMraKxczeSt+OQKc2zTwKypARKyyrY6gxOlKi6Zdl52FXRDgCcWzZB7+5C/s6D5O8+TMrQsaQMeQ6nmHCiFs0g/Ns3CP/2DRJH3ELmOeU8C3LycbVSf5W8daxkp7fXc8sTA1kw+VuT3fHdyazp/wpr+r6MLCszzZQXZ+fjaKUel+TkU2JxkWvv6ULwwC4c+eC3SvZ2BkcKzihlXFILTQA7oxNBt3eg6FKu6f6hncGR3PJyzsnH2cr5V2fT9N4e7P1qGaD0CW2e7M8ms/phrT+xpKZ9zpX2qauutbpenVZ17eHsrqN8P+Q15g58BVkm8Qz2Mx2/Ln1jOTnpmfz8+Cw+6z8Ro48rDWJCrGrVpH8pJ7hVI5zdjaTuOEJkl2ZkXsyktLSUFh3jGTCiH+lnlImb3Ow8vHw9Kx0PIC8nj9zsXKvbBt3fn9Sjp8g9cznfnK3lrcGxks25zQdxD29A0//15FLyaQozLx+j8+sj8IoJ5qXPX+L1eW/QZ8TtXFD7hfzsPDyr6G/yc/LJy658IeJsdKZL7wRKS0tJP6ucc2F2PkYr5WVvqFinzG2a9OlAaUkpUu237A2OOHu64ORu5HRSitXytzc4kqX2K0r5uFtNazGgC2mHT7H2o0UExUfQ/t6e7Pt9MzqDJ8LBgM7BgCyq3Ob/K0gp/7bPvxktBt8CNR4eqgiJEULoAAGUAW2AFMAIZKv7GIGBwFIr+74OrACOASuFEF2llBkWZvvV2fvy2PsqkVJ+jHI3gHvDBpr8veul/+Gg3gJ2NDhR1YI8hXkFlewGPTOML8Z9REFOPk07Nyf6tnac33MMB6N1naK8QuyclJntcpv2j/Rhx5ylFGZd7hzL04LaRiN0QrV3NP1dQTO3wHQL25rNby98Zvrb0c3Ag3++Qd6lHMXe4ISoYmaiKK/QFBpRbmeetnXeKnwjA1kz+zdKi0ro/dJwykpK8Q5rwMkdR3BwdqB5746c2HaYS6npNdYtKylDb29PYNMwjm7YR3FBMXsXb6HXM0PY+OUShBC4+Lhx7lBqBY3CvMLL+VBNGsA7iU9RmJNPp/tvJeLGeG4Y1oNVc/6kuqWYCvMKTD46GZzQCcGpA8cpUS/Aziafwi+sAY5GJ479tZucsxc5s+0IjW9tX0mr2Kwe2BudQAjaPdKHnRb1IOHZISx74XOKcvJpPfIWIoZ0pzSvAKH6oVP3tYbe3YXgSQ9w9MGKMeapEz4i4OlhuN3Ulku/rjWll+ZW1LVW18p1Ayc+yImHXweg4EAKUs2Di9/+gbC358KnSjxs2oRHTHl2pbZladfj4X6snrOEfLP8OH3gOI2zlKZblJ6FUQ1JsDM6IXQ1m39pOm4o+16biyypfKemJLcQnepHbTQBirPy2Pb4h7SZ+TCeLSLI2JFMSW4h+vJyNljP0ypthCCwYwybJ38PQMtH+rDny6UUmeWHtf7Ekpr2OVfap666RVbqeqW8q2F7SD9wwjThkZeeiWeYn3r8uvWN5WScOE/52CMnLRODV+WH0GvTvzi7G+k98V6+e/hdAL5/fCZ/5Z0GoP+IfnTr3RVHNZTF2ehcZbuoChc3Ix7eHhw/cgJ758v5Zk2nOLcQOwubtmOHsHbs5xTn5NNs1C1E33EDB75RJn1+7D6W1mMGsfTwQdb+uoZREx/Asbzfq+IY1ZGblcu7T73D+ys+ILJZFPn7tyj1xUpZFOda1Ckzm6UTviTh6UFEdm/BgUWbKCsppeOjt/P98Cl4Nguroo4WmP3WOCKEsJoW0DSMrd+tICctk10/r+OmZ4Yw96F36TqyEzpnD8oKyocrGv9ltBn8yjwA/AbkA7+YheiUh+v8BnQHegLfA44oLcUd8AVuACo84SWECFT3lVLKsVLKD1Fm/J+zcvwW5jP4ZjH4NeZYUjKN28QAEBIbRnrq+RrbOTo7EhwdgtDpsHOwM8WO+saGkJmaVknjXFKK6fZzuU1IlzhaDu/BHfPG4RsbQs/JI01pYQnN8IoKpPfkkfjHhFYYKJdzJimFkLaNAaq0KSe6R2tObj5osg+IDSHDip8Ap5JSCGsTXcHOWtqQtx+hQUwIp/ekENOjNWf2nyAgNoQT24/gH92QpW/Pr5VuaUkJjq7OXDh+lsC4cDJS02jYPIJLaqhSaLtozh44Qahq30DVOJ2UUqM0e2cH/KODETpBcMtILpxKo0mnZiQOv5lBE+7Fzcede958qFJ+nEhKJqqNEjrSMDaM9NQ0Rrz9GA1jQhE6HS16tSd1/3FO7DlKQAslvj2sa3Nyz1lek1asB36xIWSlphHaJY4Ww3swxKwe2Dk74tOkIUInCGgZCVKSn5SMi3qb3Dk2nCIr9VXY2xH+wbOcnjyH4lNK+fo9PACvgd0A0LsZKc2sODNYsOeIKSzHKaZq3eBZYzn71pcUn1Z0G057Gqcm4aDT4dazIwUHjprsTyalEKnmf1BsKBerqGvW7Jp0bsYNw3vyxNwXCYoNY9ibDzL87dG4xYaATuDg44bOQRkMuDcNIe+kdW1LfDrG0HTCnXT5aTzuTUOJeW6wadul3SmmsBz3piHk1lCzxeT/4d1BqRv27kaK1UHppd0pBKghN96xIWRbOf+0JOs2Ae2jOb8j2WQXlBBH3L09uP37cXir9cNaf2JJTfucK+1TV11rdb06rerawy3vPoxvTAhCJzB4u2Gnlv/V9o3hnZoS1S0eOycHGraM4syeY5Vsatq/6O31DH3vCZZMmWvqswa9/QiRMRHodDoSbu7Mnq17TWE5kbERnE09Z9WvqujcsxObVmzm0O7D+Kv55lVF/UrfnVLJxs7ZES+1X/FtGYmUkvhHetNoYBcAHN0N5Kr9w5GkI6awnPDYcM5X8ftojYdfe4Sm7ZoCUFRYRMMI5VkDvxjrdepsUoopdKfcpv1DvYkboPjl5GagMCsPnb2ehm2jObnpIFmnLtAgNpQMK2WrlI9S/uU21tIuHj9nuhsUZPZbI0uKEHo7yvIrh7H9l9BW0VHQZvAtkFJ+gLI6jgkhxCY1VKf8eyTKAN8BJVRHBywDfkAJ57FsmReAd6SUy82O87YQovzpnfK7AkgpfSx9EkI4oNwxqBHblmxm3PxX8fD3onliSyb1f57AqIZ07JvAj9O+q9bu/PGzjHxrNN5BPhzddQTfyEASJ9xFeGJzFo1+j85jBrFu6g8mjSOLtzH0h/G4+HsSnticb/pN5IAatw9wx7xxLHlutum7g4szD22ZSVFOPrG92/PT6Fl0GzOYlVMvD5oPLNnGffMn4OrvSWRiPJ/1e6nKc43s2pytXy3jlkn34uzvSePEeN7v/yJ+UUHE9+3E0mmXdfct2cqD81/EzcwOSaW0swdOcMf00ej0AicXZ5p0a0HjxHjWf7mE1J3JdLinR610/aKCKC0pJT8zh7B2TXD1dcfFx4MP+00AoNEN8ez4cS0JD/XGzd+TRonxfKRqjFQ1qku7cPwcA956EI8gH05uP8xXL3/GY3PGcXJPCm4+7pzYm8Ky2Yvo+/RQFkyba/J755ItjJk/CQ9/L5omtuDN/uM4deA4909/AiEEu5Zt5cC6JI4nHeWNv2bxwKYZOHu68PsTH1itB3f8MB6jWg++tagHQ9R60CA+gl7THsAtyIcz2w+TsWAN6ASNf3gDe38v3BJbcWz0VALG3MWZqZdX4vC+4yacm0XiP3ow/qMHk/71n1z4djFh7z+L99Ae5B88TvbqHRXqRtbSDYTPm4K9vzcuXVuT+vgU/J66m/NvX46e8xzSA+e4KPweuQMeuYML3/zO+ZlzCX53DAhB1rJN5K7bZbLfvWQLT85/GXd/T2ITWzK1/zgaRAXRpm8XFk2bV63d1oXrTNufmPsi3479iIDGwYye9TgIOLdsJ35dmxE38W78b4xny0MziXluMPsnV15VyJxlnZ82/d3lp/EV7E//sZUbFryIk7+nSTP2ucHsu4Lmofd+pc3MR0BKzv+VRI4aqnf6j610WvgiBn9PQhKbs+zR92j7zCC2vHW5LqQs3kbfH8ebbH7uOxGA4K7NObPp8nMSCwe9avr79u+V+uHg4lyhP6mPPsfaPlejW+5jeV3/rQqtmrQH78YNuW3mIyAER1fsIviGOHpOuIvIxPir6huXvf4tgz98kjZ334QsKyN53R5uenowy8z6rf1Lttaof2l9RzcCm4WTOLofiaP7senrZayc8RMvzBiLEIJ1S9bz3fvzmPHTO3j7e9O+W1seuf1xQhuFcFO/7nz61udWfTSnbWJb5n34PaeOnebBn2Zi9PekYbfmrHjkPdo8M4itZvXr2OJt9PlpvMlmwe0TyTp2jq7THsCloQ/nth0m+ZcN6Oz1dP/gMaLvTCTjYCo7VisLG2xcvIHJP0zBy9+b1omtGdPvaYIbBdO1b1e+nvp1VS4C8NOHP/LUu08hJWxdvpV2N7Uj2N5IRGJzFo5+j4Qxg1hjVg8OLdnG3fOVOhWR2Jyv+k0EIej3/mPED00k7VAqKauTaHl3d1z9PWnarxOR3eJBJ/hqxFt0f3owy6dVLP/71fJvlBjPx/1fAkmlNFkm6TflAZr16YjeXs/ch6cDoDN4UJqfSU1m77+YNeWKNhr/bsS/PYbo34AQYq+UsqnZd3eUWfyDKOE2s6WUdwohDMBilNj9C6ptBrCtKmngQ+AvYKGUsoOVY38DNJJStqvOR/MQHQCDm5G4hHgObt5HpvpAnzWuZNfG1ZuwhGac3HTA6sNroMyOXMnG0j4gIY7jmw+YHjazxMnNQERCs2ptLO0b3hBHyuYD5FRj7+RmpFFCRTtradXZ10W3pLCk0na9FY2ohDiOWWjUJM2cNIoxuBmJSWjO4c37ybpC+dfErpmrJ6EJzUi9Qj24ko0lN6Is3ad3N+Ka0IKcTXspqcaPmmJvp4Sr6NyMuHRpSd7mPZSkX73uh2VGnN2MNEloxpHN+8mu5jxrandTweWaYO9uxLdrHBc2HKCwhnlYFeUzAvbuRvy6xpFeD5oAl3xcCE5oxulNB0wPx1ri4G64oo0lOWpEQk36k9r2OfWt61SDul6X9lDq7nzFfq+mfaO5XUYVdb8u/Us5a0sqzny7uLvQJqE1uzft5mJa5bt8NWWEewQNE5px5gr160o2lizUXTL9bXQ30jKhJXs27eHSVfQ3RncjQxO6cLKasnB0MxCe0KxaG0vbQ1v2V5n3Tm4GItVyvVxmldOsMWHrKzU8s9ph7xPxr3oxp4Njw79tYFtUmPqvOndztAF+PSOE0EsprS9bUtlWoJRBlbPzQghXKWXlBYwtsBzg1xfNyur+Mo/qyBP1726+DTRtieUAv75IMy3IVH80Lqu8LGN9UD7Ar2/KB/j1zYdl9b+6hPkAvz6p8S2/WnLe3jaRnTn/2p/JytjK1Vwb9WFFNtC1HODXF3dhfenZq8V8gF+fdLbBOyht8fsI2gDfFvybB/haiE49U9PBvWorucK9spoM7jU0NDQ0NDQ0NDTK0Qb4GhoaGhoaGhoa/wm0yBQFbRUdDQ0NDQ0NDQ0Njf8Q2gy+hoaGhoaGhobGfwJt/l5Bm8HX0NDQ0NDQ0NDQ+C/xd77SV/v8Oz7AA9e77rXk67Wmey35eq3pXku+anlw7eleS75ea7rXkq+21NU+f99Hm8G/PnlA072mfL3WdK8lX6813WvJV1vpXku+Xmu615Kv15ruteSrLXU1/ia0Ab6GhoaGhoaGhobGfwhtgK+hoaGhoaGhoaHxH0Ib4F+ffKzpXlO+Xmu615Kv15ruteSrrXSvJV+vNd1ryddrTfda8tWWuhp/E0J9mEJDQ0NDQ0NDQ0ND4z+ANoOvoaGhoaGhoaGh8R9CG+BraGhoaGhoaGho/IfQBvgaGhoaGhoaGhoa/yG0Af51hBDCz+J7fB11fIQQbYUQrar4xAshXOqoHXqF7QPrqCuEEP3MvkeY/a0TQjxfF90rHLNTPWjECSHa14c/qp4QQrSpYlv4v03XQken/u8ohLi5jhpu6v4OVj52V+lfveeBLfO1vrXVdtSjmu2OQojo2upeK9jq/G3V39pQt9pxhRDi0dpqmu1rq/7L0eK7nRDif3XVs9RV/a6XftxG/YzN+kWNfw7tIdvrBCGEHlgmpeymfvcGNgM3SykP11KrOTAUKLC2GQgFmkgpaz3AFUIsl1J2F0J4SCkvqWnuwHPAROA3KWWVP6LV6AqU8++u/gAtk1LeaHncWmoeAc4BWcA3wGOAI/AoMAXYAoyXUubU1l9V3w5YC7wnpfyqLhpWNAWQCswDjgKbpJRbhBCtgVlSyo7/Bl0hxNdAMSABB5T87SilfFEIMQWwk1I+VQc/LwI7UeqpJQ6AXkrZoba6qna9562tyssW2qreH1LKm4UQd6K0h1R18z6gFbBbSjm+Fpo6oLuUcmkV2x2BMCnlwVr6Wu+6tjh/Vdcm/a0NdZeYaQogAKXvDpZSflCXvtZM2xZtTA+sB/5U/bwX8Ae6SCn71MVPM13z39w1UsqEuuqZ6doiD2zWL2r8c2hXZtcJUspSIUQJmAbM84Evazu4V7V2A7uFEN8ChWabdMBYlEHusDq6WiaUme+PhRCDUQbQK4HPpZRFSt9We6SUUghRqv5dVp4X5setg+xJKWU3dTZlIvA2MBLYDfSQUubVyVlACFE+qP0LGC2EuB04qX5+llIeq4OmUPPhCPARyg/4LUKID4EiYHAdfbWFbkPgcyAD+D8p5R9CiH5CiO9Q6kStB/cq26SUPYQQeillqdk56NR68bUQwiilzK2NqC3ywFblZSttVa+VEOJ9wFXVbQNcQBmIHJBSzq2tLPA0sLS6QTNQq0GzLXRtdP42629t2I/fAzipfzsCbupnMPABdetrbdYe1N/GfCAZ6Ae0BKYBVzWgVXWlEOJz4EOUOndV2LBPsEm/qPHPog3wry/0Qog7gIdROrBngEl1ERJCJAIvoHQwAjiOMmuTJqUsAV6vo49SSrleCHEf8DXgAkyTUpavyXs1nWQ7IcR61V+EEBvMtmXXQa+pEOJ14EvgDqC36p8n8KwQogWQqOZHjRFC9AReBb6SUs4UQmxGGYwEAo2BOcANdfD3DyFEDuAORAGtUQYgvwJNAV8uD3L+ad1ilDLJBEqEELOBHOA9oB3KwCG/Dr6W158/1Dsk8cAuoDngI6W8uw6a5Xr1nQe2Ki9bau8G3gJGqBq/Ad5ACyBWCPGXlPJMTcVsOGi2iS71fP7l2Kq/tZFuL6AtSluzl1I+rB5rrLq9rn24LduDBE6h3DHtD0y9Cj/NESgz7aPrQQtslwe26hc1/kG0Af51gFBiCUuBRJSB0beAF+AuhBgBOEgpP6ql7Agp5b1CiJdQZpW7oPxQzhVCvCeltHbbt8ZIKbcKIQ4D4UDY1WiZsUWdpdBJKSvMIqm3lWtLMrAIeAdltn0s4AdcRLk4edzyODVEAL2llOfV7yVSyhPACWCjECKsDpoAg1Dy8lGUC7szQF91pikM+EwI0V3WPm7PFrqeKD9YWYAH8ApKvnig/Og0Bp6tpZ9w+bkjIaW8UQixVK0TVkM1aoEt8sBW5WUTbSGEPUpY2jHgd5TZ3yCUPE9Bmb2dJ4S4sZYXvTYZNNe3rg3PH2zX39arrlBCnyKllI8JIT5CvQMilGenGgohHkEJ2akLNmkP6qSXBIKBuSiTX2OAACHEEJRwwG9rqemPcpFQLKX8E/hTCLG6NhpVYKs+wVb9osY/iDbAvz4woMyIgnJ7VKLcQtUBzoC+DpoNhRDLUQZaT6HE6S1G+YHYJYToI6U8VFeHhfLQ6zkp5VAhxGtCiElSyhexHiNYEz09YK9+7SOEeBzloqeu/gmUH+wNwADgfillM/U4etXPR4QQH0kpi6uRssYai/CeCCHEeGCxlHKLlLJOd12AR1BmfS6gDG7eBVKEEN8AccBzdRws2kL3R5Q6a4fyo2v+UNk5INzahVp1qDNT5RdNsor/64ot8sBW5WUTbbWePy+EOIcyuF0L/IBSfhtRQh+SajO4tdWg2Ra6tjh/M2zV39arrhrO8bAQoiMQC0Sref0dShjQKSqGA9UGW7UHfyAEiAAaAQ+gXOQ4oVyMOFa9a5VkAV8BY4QQPkDnOmhYo97zwMb9osY/iLaKznWAlHKWOkO/DqWjHQLsBTKklO9LKWfWRk/tEBZJ5UGpTigdSyfgLynlKyizCxPr6q9Q4s/boISlIKUcB7QUQtxIHTscNa7wCfXvBVLK7lLKnsBMlPjbWs20q53ou8By4A/gFSHECmAVSijNEOBsXXwFBgshtgghblK/p6OU11ghxFxR91UN3NT/O6H8aNmh3IbdiTIrtPdfpNsRJURnPkpIUjBwG3BKSvmalHJkbe+OlIccCCF8UZ71WIYyU7sUZfB1NdgiD2xVXjbRFkIECCGGAXullMtRHjRfhtLv9Ea5C7OpNppSymIp5fMobcmIMmh+GmXgdY46DpptoWuL81d1bdLf2rAf36j2rX8AA6SUnaWUs1DCfhagPFdTF2zSHqSUM1DuXhwFclHi5TOBZCnldCnllDpo5kspl6g+LgAa1MU3K9R7Hti4X9T4B9Fm8K8v8qWU7wsh5qHMLqyso86twFChPKiaidIxXgLuVia2ESgPh9YJKWURYLkc5iiU0Jc6IYQIACYIIYajrBwRgjKob4vSWd5XB9ltUl3RRwixQr21+R1wGmUmpaguvkopvxRCrAJmCiHuRlmZ42fgZ/XOw/sos0y1ZSnKuQ5FuTDpjxJrmY4yGHmB2j+saCtdb3X/11EeHiu/Hf2jEGKhlLLW8fdCiKHAG8BTUso6LbNZDbbIA1uVl620o1DCqhDKg4UpKAOmhigDjxCUkLYao7bbbqiDZiHEMeAhlEFTb5QByJu19NNWuvV+/iq26m9tpdtOCLEApT6FCSE+kVJ+z+WZ8LrOCtuyPeiANJQVdIYD9wOf1lHLHCml7AwghHhKCNEf5WJiZx3v5NR7Hti4X9T4B9Fm8K8T1NARBwAp5QWUjuFGUYd15aWUC1FmU11R4s+7o8SH55p9MuvoahshxHr1s6H8A/yEMjveuo66fYApauhLZ5SOcjnKj/gdKJ1kbYkQQvyl3uL2Vf93QemAtwohPhJCeNbFWSnlcSnl7SizM43N0mcAPkII5zrI3ogyWJ4FRKNc5B1BmXH7CogUV1jD+m/UjUD5sXVHiY+eiTKbL1EG+X/Wwc8lqn8bhBBrhRArLD6rhPIQdl2wRR7Yqrxsoi2lXKPebQtACV0DKB/YdUGZZazvQfMF6jZornddG52/zfpbG/bja6SUfVH61zFAPyHEkyh1DdTfoTpgk/ag3slwRnl4PwWlzF5X0+qMGsb5llnSZyi/MyOB7UKIF+oga4s8sGW/qPEPoq2Dfx0hhPCUUmaYfQ8HSqXyAGddNb2BkVLKyfXho6oprMURCiHeAr6QUl5NaAJCiEellO9djUYNj5MAbKjjTI25TsjVlJGFlhFl0FyKcgcvVEq5Xt1mevfAv0XXFqg/gC5UjgXWAS5SyrQ66tZ7HtgyX21YF4agxFpvR1lq8BjKg/y1WqveQnM/yiD0TpSBUluUQXoy4CilrMsdLZvo2uL8zbTrvb+1la4QojvK+vIC8JRSnlLT+0gpf62j5t/Szwgh3ICBUsrP60PPir4T8H8o7zjJquW+tmq3NukXNf45tAH+dYQQwk9eXpmlPO1hlFjDuqwigxDCIM0eCFVnLXqhPBBap8olhGiH8mDaMS6vnFJ+K3KUlLJ3HXWbo8weCeCMlDLVbNu9Usov66AZjPJgVvkDu0fKf8jq6KNAmU05DPhLKU+rHe8LUspX66pbg+OGAa5SyqR/g64QojfKTJU1SlFmrOq8JrMQYpis5coYV3GsMOo5b21VXrbUFkK0kFLurOO+Nhk023IwbuVYdT5/M416729tpSuECJdSpqh/3yyV1WQQQoRKKY/X1dcqjhXGVdRZNWSrUEpZKQxUCHG3lPLrq/SvBcrSqwXqd6uTWDXQsUm/eKUyEUIMlFL+WFtdjX8WLUTnOkAor6F+HGXps35CiHuFgg71LaxCiKg66I5EiQc3xw/l4ayrmfnYDuyQUt4olbcAvovyVskFKA/E1ZV3UeJO70V5CBYA9dz71lFzAMrLRRJRZgAbXoV/oMzIzARigJVCeQlNY+q27n0lhBA/CiHmCCF6WWyahrLqxb9FdybKgKsjykNvfVBCqTqizLa+UUc/HxTK0rB3CSGGW/lczZth6z1vbVVef4O2Xv1fp/Y1n6BcpNcJKeX3Usp1Unl4caWUMkVKeVAdONUZW+nW9/mrWjbpb+tbVwjhqP62fCCEaCiEeAN1wQQhRFOUfriuvtqqzvZECdGxxst1FRVCLBFCNAbuAroIIboKIdoCI4UQdVkb3yb9IspvF0IIDzPf3YUQrwtl0YuH6qir8Q+iPWR7fTAD+AK4BSWm+36U+MjXgfYoszRH6qD7GcpDWm2klFsBpJTnhBB9UeLla40QYh2QBzQTQqxBeZmRAByEEKOowwNaQoh4oAlKLPcelIfd4oQQXdTv9wAfV61Qpe4AlNURPpBS7hZCdJFS1nqVDHOklMVCiFIp5R6UJeY6ouRHfeEFvIjZuwWEEBOB01LKef8i3RSpLgcqlJfxzER54cp6IYSBy0ue1hYHlGVM9VSOsRXAbHWmtbZLm4Jt8tZW5WUTbSGEvZp3aUKInSjtoxfKkpQlQoi7pJTf1FFbL5X1vnUo/cDHKA+076yLni10bXn+2KC/tZHuHygv6itGeTj+HdTnHVDiz68mPNIWdXYryuo0xUII86WYC1AG0VfzThcPlMUcAlEegF2FsljA/4Db66Bnq37RJm+Q1/hn0Qb41wfT1f89UJZCS0fpfL9GWRLtjrqISmXN49EoDxL1MdtU5ztDUsrOQlk3eRjgJaV8p3ybUMJh6kL5cmLmuKI8sDsFZbaqVuEvQggvlFn1S2bJ9RXv1l5UfKhJAE3E5bfwGqWUzeuoLVHCf9qoF0w9gLVSyolX47ANdFsJITaqupHAz0CWUJYi/UJKebhOTqpLwqq3pD8SQrgAbaWUK9X0dOpef22Rt7YqL1tpfyGEuIiySsiNQoiVUsqTKAOHUJQ11ms1wLXVoNlGuvV+/uXYor+1kW4flJf++QL7pJRJQgjUyQpPKeWyq3GX+q+z3YB+KBcJy8sThRK2s5DL75CpC2UoA/v3gFellJOEEG2AQ7JuMe026Rex7RvkNf4htAH+9cG9KC/t8EIJITkKPCClPC6E+EQIMVTW8rXsQoi9KOuUS5Q3/pkPSJ2BX+riqFBWNPgVJc6wq6i49n0plW8lXxEp5WZgs3orug/Khc5alE5sF8pa1R+jvNGyppoXgSeFEE8AbwohMoGm6u3oA8ASWbe3awJsllL2AlBv754CfpHqkpx1QZ2daY1SDwwogxk/lPXl6/K2XZvqSik9hBAjpZSzzY71CMrM6lyhvECs1nddzLATQjREqWuzUJeMlVL+UFshW+SBrfLV1tpSyruEEJ1R1nwHkEJ5YPFdlPdQ1KVN2GrQXO+6Njp/W/a3ttB9AGUSyQicEUKMRQk5fBolVKUuftqyzmYLIcpQysqIckd3g5RylzrgrdOzOkKIR4ESIBU1/EcNzxHAc3X01ab9orTNG+Q1/iG0GPzrACnlk1LKh1Aepv0A5dbgg0KIMSgDnEeE8mrt2mg2lVJ2kFJ2BN6SUnaSUnYC5gE3SinrFLcopSyRylq8O1Fm1UdIZbnIfcDDdRmAmTEIZdm2B1BmgVaqx/gZZdauLnyKcnHwBErM6nyUC+evhRDvi1re21QvcPRCCB+hvHBkGhAKdXuDrxnJKD+Ozii3zT+RUj6tlp+LEGJ6tXv/zbpCiO3ANHF5ydT1wASUH8w9KIOvqyFJKg9Zd5RSXu1617bIA1uVl621kVKuM/sqUJ53+UpKWT6QrK3eXSgP3ZuShPJc0Wco78ao06DZhrr1ev6qpq36W1voHkd5WLkpymov21HudHqhhITWBZvWWRU9yox9BMqzaaBM1tSVwyj+fsH/t3em4XJVVRp+vwwkzLNMosyiKNIKSiASwAAiQ5hEQGYQcGhFsBVBEBEFBFHQtlVkdkBkbgEJYR5VQEBttFFauxGReR4Skq9/rH1yK5d7k9Sp2req7t3v89RTp07dWmffM66991rrixCdMcTM8XFEvHzT5L4vqk9B/r3E9VCpppcYnV7EdnmNkBdwVXq/mxgN2YIYwd6gRbu/aVjeHziupp1/IWIKK2XB0xteNxMOft023gxcTDjkpxKzGCcQSWRTgRNbsH1Ner+83/qtatgS8M60vGJ6HwV8EvgSsHYbzoMt0v++QMO6c4Bdu8UuEUt6Q1q+gcgbuZTQGBjbYjv/AfyaqHl9O3AnUf/76G7ct7mOVw7bRALgJcD16fP1hGN3PKGK+rcW2lrZvIGI5d6scX032M35/zdsoy3321x2CR2AM4k8r8sIJ/cKIv/lWmCxbjpn0+/fRpSb7L9+K+CoFuyuCryLKO7wh4b15wOr1rCX5b5IPAMXIJ6RYxrW/ydRe39qO86x8hraVwnRGVksnEI+TCTRPEDU4m2prjyRCFtxLhEOc7rtJ5q087/ElPgM4obVWLbyAmLa+z9qtvFZYgR/CeKBO8H2FyR9w/Z+qiHkIelXhBjM2yVNJZJib0vrxhIqkE2NqNi2pB0l7WD7y5K+Rl9d4qUJNdsJrl/reH9gU9t7p/AsE/GyR1BfRCyH3c2AxSVtQhyztdL7866XANvIA+4Lydgstf8G21+Z1w/nRo59m+t4ZbS9DKE6/fs0Er4M8ALhjDxNqtbRZDs3BD7XsMqEE32YpElELHLTZLLb9v9/ANp1v81l99+AM4CjgFOAjxEluadL+jZwMHMKQM03ma6xFYlBn30kfQ+o8psELEcUaKhjdxzxf56RbD6mqIoGMeu7B/DVJs1muy86g4J8obOUOvgjiPTAcXotSFSTWRQ4DzjN9sy5/Ly/rfvoq+6yYMOykt1T3JAgW6OtOwGXuuEElbS17atr2vsFMYJ2ItGBWNP2GpIutz1FqZJGTdtTbW+pqKm9mO0fptCcc23v3aStdYEP2T46fb6fKFFWTZG+HfhP24/UaOeXiByMTzvVvFaI0RwP/MQpAbUb7Kap4X2JKhbV/747McOzFbCvmxSIabD9MOHI7ZTeaVj+imskv6V9sDLwqTbugyzHK7ftZGslIo9mFNFBvYCoRnKi7TubtLUtoQz7e6JCy/rp9T7CaX7CNYTgctlNttv2/yd7We63Ge2OBqbZ3iyFz6xje3Jyeq+0PbmGzVz3r88D99n+paSbgUnVcyd1KNYFPtP4LJpPu3sBj9m+RtK3iHPsHiJU5zmi+lqzz4cs90VFLkoVjiTmDCUTsJbtpZu1W+gsxcEf4SgSwA4hMuZrObgD2KztLOcgOds7eQChDkkH2/5+i/Y3t319ymNYzfYd8/zR/NkdA3zN9ufm+cfzZ28R2y8MsH4h4IOumd+Q0e7mtq9v+PxJ29+RtBnxQK41sqRQGJ7Z8BpFPHRHE2EKTZfFy7EPcu3X3Lbnss0VAFwz+bzdTnNuuwNsp6X/fy52s9xvW7WrBuV0SW+z/V9peWnbT9awl/2cTZ2+K/sNLO1o+9IW7Y7vf1+RNM52f9XY+bGV5b5YGH4UB3+EUN1MkrP7HrdYrz2XzVykNo5LH2e044GoEG2Z7vqlyYYcSasCk4l8jL83rB8P7OAmqyk1/H4MEY7wMvCi7de6ta0NdhYnBNTua62ls+1tQ4RRDcQfbN/dju2kba3nFlVRc9nOfdz6bSuX01zbbsZrbBUi/rzr7UragchJanSU17HdajjoYNvLcj0oRJ4+bvtbLdiopVpbYzst74M08/JhD5HKdyEvpYrOCCBdtL+EiPEm4iK7zmZm1gL+m4iLnwIg6T2S7pd0taSTati8hlCxJdlbXdKpko6RtFtyeptG0jclnaRQEez/+koaga7LisDGNCj3SnoDkQzXSk7Ou4GriHFJLuMAAC8oSURBVNjSyyT9UtJVkqYp8gi6qa0Vnwd2kFQrhnsADifirV/s9/4ycH4L50PbVVEz2277cZO0qqSPptH2Rp4GJtVsZy67uc7blXrI7idsW9JYSVUe0vdaaONs2n3OSjogvf9E0jXp9VtJvyRC9w5vsckvSXpQ0vOS7lOEKbVEjus2DYKdBrwiabKiCtxS6bslW21zYegpSbYjAIdKoyWdTdxkWx5NyGFzCPgZUW1hbUlXEw/xo4kqKj+WtIAj0Wh+edD219LN9goiYeohoiOxDbAmffWwm+F9xEPlW0T5TREdqMOJSgenEZUZmkKh6DuGqJjwYOrUrEzUkz7M9v012trIxba/mra1uO1n03KdBOasbVXE7r4H+ADxQPtMq3HnALYvUcTJVjMYIsoGHgnNlZpTXlXYXOJRuY5b5TSPJWlhJCf0PEKYpy5ttZvr/+81uxUOZe5KXbWlWb2M18O3UxufII75okR1mj2IYg9NhxP14y7b75N0i+1WBmdyXrdbAnsSVeWWIPIO/kHU1v8TsK6kTYdiJqLQPsoI/shBhIP7yS63mZP1CdEvCLGUy9Pyj4Atm3TuIXVqUrjPAmndA4Tz/XXCwa/DU7ZvAp6xfXNafjotT6P+SM0korzcdkTM8VQi8esx4IvVaE2zSLqAUIKsEtO2Aa5SqMQCbNstbU3t25yo9LFTCiX6LbCmpAsVYTt1bDZKxG9IzBTtQEjT72H7Mjdf5eIcRdWRe21vTtSn/j+HkM11ROe0Lrlst/249XNCf6OY3foJIUD0Odu1HPxMdnOdtz1hV9ISkkal5U0U1YiWUKr6ktZtXrOtuc7Zm4B3EmFVOxL3qzcRyazPELNwrdBOp7jt+0ARirUPIfy4AFFlriohfBYxw/OB4tz3HsXBH+ZIWk7S+UTc+S9t79WNNoeAvxMj4jcQlTNmETcxiBJjddQQBxuR3SLFsu5bwybAipL2BpaTtLekfQiVyWp5g5p2zwC+QJQi3dEhy/6U7T2Ih/pPa9r9FCFss6lCKGUmMLlKiKuZ9NX2tkpaQNLJRCjCFGCipD2Bl21/ihi9ur6hYzK/dtchHoZVmMdjxDn2XHqvJRLjTEJMmW3nOMd6wrlN5LrGesXuLoTOymrApsTsyMXErORF6X1inYZmPGdHEUq7fycEtR4i1H3vSutqlZ+U9FlJLRVw6E+OfWD7lWT3I8Tg1KbEvet5YvZiZ9svD26h0K2UEJ3hz3OEqMZnJS1D3HC70WZWbL8g6Vyi5n8tmfCBzA6yfmbaZl0J9VFEybrRhHqj0vKCabluveMx9D10T1eU9TxUUZpzWeqNtGP7MUk/BH5F1Ix+mnD2rwW2t33JXA0MXVsXI3IxHiDqW3+c6IQdlP6PqyVd1+xMju0/pI7XYQr14VUIZ+Z5ovTmPTXaWtm+TX1iyHOooipqgNcmk+0cx+0MQpBvUcIJPVbSNrb3kLQf4YRu1SV2s1xjvWLXUSL4KmBvohN9FnCW21TlJ9M5a6KT9zXgfuJ8mGD7ZgBJzc7sVtwJ/Dutq27PQcZ7wpGEHsCBRO7BQsS++UgKDfpIC7YLHaCM4A9zbL9seypxI7+ciNnrOpu5SdPx9xAjTBOJG+MmrZvVQZKOJNQbJ7Ror+JhR+nOR2z/IC0/bPv7tr/XQoWDI4gH+QrEiOWLRLxlFX9aq6OWpuSXIaaH1yFCXiYQ58VO3dJW20/YngLcS+Q07Ox+Ij41wrSq3/2X7QOJPIlHiEoU2xHqw9fP9ceDIGlDSY2do0qI6f2SjqWmwFNm2znOscoJ/RRwV3JCj0qzRe+gdee2nXazXGO9YldRQWgN2ycSlXmWIQYnWibHOatIfL/V9vNEKE7l3D4j6WJJRxGq6k1j+9Z+I98th7jkum4lHUSEJ+1J+IX/Qcxg/B54lBC8KvQYZQR/5GDbGwNIOkzSjsSN617XL2mYw2Yu9iAe2icTdf+nECPif27BpokRn9WI0cBlqfkw6MdKCoGVFdK70vIE4LeuUac9cRMRX9lYxeEFYkp9OnAS8N4adi8iRql2IfbnmsS+eQswQ9Ki6QHaDW3F9gWSniAq2+wBNC1s1R9J7wX2t32wpPOAD6VRtoUIp/FztpsNd8ipiprLdo7jdgQRllY5oeb1TuiNNdqaw26u87ZX7K4AHC7pFCJ+HWCzhhHnUcBY23UqFLX9nLX9mqRTJB3kUDZfg5g1nUXcdw8jcjJaYe2U27G2oijF74ArbNd59uS6bh8icumWJK6JfYictQuJsJ83EzOfhR6iOPgjAMXdtVEa/CwiqWgrYGNJP7XdVCnDedjcSNIFzdrMzBeIiginAscQSXS3QAio1LT5ViK84+DGBEpJC7fY1lOIihPHE07HGGJE5SNExYdfE4qpzXai7iBG1tciHujjiXCizQlHtO5o+y7ptxcle3sSD8VlCId/EvCLLmkrALanpdj5o233l2dvinQtHE6cWwCHEknWIkpkHk/8H8228RfJ/vr0CTEtRCQIv0KoMtdSWs1oO8dx6xXnFvKdtz1h1/btwBRFJaJ/BT4MfJFIAIW4JsYO8vN52c51zp4F3Jmu4x8SM25Vj2RpYsb3ukF+Oz+8izk7kP8CnJBmDz7uJrQWMu6DB4j7l4BbiCpKNxOhVv9HVAIrDn6vYbu8RtALWA8Y3/B5IcL5XayN2xjfbpttatfy6X2V/vukpr1V+9tK68e22M7lgI3m8v2mNe2OJUbVRzWsm5LetwYWakO7J/db92ZgiW5ra7Izam77uaZNAZ9vp81BtrMCsEK32c5x3IiY6C8RTtZ2hPbEHcA3iI7vSjXb2na7uc7bXrPbYGs54IiGz4e381xtsNuW6wHYq9/nUUSp4lGt2h5gW9sCi3bTPiAGZTZNz7bViNmC1eiyZ3l5zd+rKNmOECRNJabgPkqEU8wAXiIc/nG2v9OC7fWAPzqFjkhDo9zXKgqVwl1dv8zeosCr7he3LelDtn8uaXXbf6lhtyrZNgE4nQj92Zp4WC4JvGD7A3XanOwvRjywnqlrYwCbW9u+Oi0vbPvFhu/2bGEft62tCr2CHT0XSXtJX7Fdq9yepIVsvyTpXUT+xKMN363nGiqTyqgKm9l2O4/bWMLJeNApcV3SFNuXS9oauMn2S91gV9Jytv/Z8HlV4Enbz6WZvcNs19HGqOy1/dptt910nZ1k+7MDfHen7Q1bsJ1LJbjtdiV9kxhlHyjBeCYw1WkWudNtTb+/zvb7JX3c9nfTjMYvbG9Tx16h85Qk25HDEkRc4YpEtvwkwrnfn6hl3zSSpkpaiwgdmShpkqQNgAMl9UJt/NeAj0oaI+nnNX7/MWCCpEMlfbx6AZ9MCXvH1GyXiQdAFUI3FljNkbT5cl3nXtIikqr6/NdJepukVSStIWm8pCtqthfg85I2kjQZuFTS7em1NPA5zVknvlNtnUUk0VXb+JOk6xVCLhUb1bBbUVXLOI6ohd/IqdQjp5pvDsXZth832zNs/wlYRNISad3l6f3qOs59RrsXVguSJqbPyyWbL1JTFTXXtZvxOnvPIN/VrQBWket6yGH3fYS699aE6vs1ROLxL4nwsNO6qK0QIT4A28Nshfq6leAKXUCJwR85zCIc+38Hjrd9XIrj+2/bdZMMl2DOTsONRELO/qSbRLcg6Q4Gfri8RkzB3lvD7GvptR9RB30CUUHmFSKh98DBfzpoOzcADiYSsaqbazuqLyxEOJ5L275b0j+Iuu/Ppf/hCmpWe0m8RoRmjQewvZGkw4g8hVdp4kGRq622rTnLyP2v7S0k3dCwrpUH2iupw7sU8GlFZYqxtrcgZsuaQhlVRnPYznXcFDkyxxADET+QtBexP8cADwMX2m76fpPJ7vRke2ki/OeD/e6vj0vNzXBm3K85r7PB8oNq38tyXQ8Zr7OnbN8k6Rn3ldx8umG5acHCnPcE+mYaGo9d18/EFwanOPgjAEmfIC7ah0kjK8mRFK3VhM/RacjFq8QIxz3EQ/0i4NdECMzevH7Eda5I2ohILn0jIQbyR6IG/J+BxeuGeRAlyc4jErOqGbZGoaS6N9xFiZGjXSV9ldgPG9reMk2p/4oYcWoaSVsAixNJ1uOAJRXKlXcT54jdXB3sbG1lzv3nuayrw2LAt4kZrR8Qo3Tvb8HuJGA3IsHtROKhviIxIvhFSYe4nohYLtttP2695twCC0g6HZgGfIeY4YN41l5BhNc1ey7kuh5yXmcrKMT6GhGRtFqXXNdDLrtzCBbSVw2tWn4xbbMb2lq193bgLeldwFqS7gQWtv2OmnYLHaKE6IwMHiRKQp5D3AzGEDf344Dd6xicS6dhFO0Tkmon1XTjzH7L04Gt3LxS373EjEVVscANL5KD23wj7f8DniX248LEsRodJnUt8C5FPkWzjAK2JGI3X7B9TLK5KpFQ9dEa+6BiMpGctT7wbuIhvjXwQaICRbOj4jnbuq6ksxTl6tZRlJp7a8O6unWkdyb2wYXAujXb1p9c6qW5bOc4bpUTOqPBCX02zYpsCxwA1FELzWV3OnFfOIkYSFiF+N9XJfZPnRK3ua6HnNfZOGLwY+WG1xuBBWrag95R863oL1g4nj7BwgWpty9y3hMesb0RcLvtjWxPSMsbFue+Nykj+CMA21MlPUgkaN4IbGP7euB6SedLWtX2/zRptrHTcAh9nYbPEbGG32xT84eCZ5v9gSOZ8ilCDGRnwpmdRYhnLUXkISxq+8pm7EpanHAC/kqMBD9D7NsLgdfS9HedMnOvEiOIdxOS5hWrA8em9b+tYRfbn08zN2cRnZIlbH+h+l7Sdt3SVqK+8/NER+xEwuk4jeioHkLURq/DckSuRFVytR1T27nUS3PZznHcGp3Q79s+QdK1yQkdTXuc23bale1LJF1NzGyOs30yzE5k7aZrN+d19jcPUCZZUu3iAPSImm8DD9v+vqTdbP8AYiDAIVxYl7a3VdJqxMzjwg3rRIzglxCdHqaM4I8AJI0jatYvS4wuPibpJwrxjTMJEaimcCjZ7kLE4d9ITHVfnxJA35UelN1I/3CMscBtKWa2WW4G/odw8A+xPZmQDb+ZqFZUR/1vI2IUZkuifvQphCP6DeC0NP3/7Rp2nwb+STy0q1Fq255meyKwsKRWHManiWnjvwB/SA8IJK1ChC91S1u/DexAOPOfIK6HVYnR922JOuhNY/u7hGDWB4Erac/DMZd6aS7bOY5b5YR+gRhlr1idGFzYt0Y7c9q9AWarfe8PvEPS5um7g4mwrWbJdT3kvicMRCvXRE+o+TYwh2ChpAPS8gRFxZtuaevDRMjqGpI+R4SZTiPCfwo9TBnBHxnsCpxh+xpF+bcTiYfaGCLmdP9mDTZ0Gs6godOQvv4B0Wn4ahva3i4WJcKRqpCk/dLrZWI6/WSa3w/vJJJIVwYWl/Q74ka7oe1X6yRROUpNXi1pG8L5eIg4XlXZydHpf2mWlYiOw7WAJX2JGKipZM9PIip8NCtIVY32nGP7z5I2JqaKt0g+/q40H+qQra3AH2wfqUhWO52YIVmSyHl4inhA1n2wPUV0Gs4hZrc2p2/Eto5jk03NN5PtHMetckIfJUbbITmhwDRJP5S0rZMAUBfYvU3SjUTuxX7AY8BkRZ7KZ6gXvpXreshiV9IoYuBjIGqJXCV6SfAM8ggWtr2tjjLPZ0v6BfAj4A5HuczR1LvHFrqE4uCPAGyf3/DxCKd69RWS6ow0t73TkBPb6/dfl0JdrrP9M0kfk7SY7eeaMPs8cQN/lb7RFID/lTTB9h0ttPdK4EpFZY/HbD9R11ay97CkTYFLCJXZ+4nZmzHEQ/chYI2a5r8BjJF0E+HE3E9fYvA4YD9Jv53fB1nmtq6uKIm6EHAxEZ61S/puWaL0XF0Hf6HUyZlK1BQ/peG7Op2ynGq+bbed6bj1jHObRmU/SShbz5T0EaKSjoh9MBH4sqS93aB8PS9yXQ8Z7c5i8Pv/Zc3aa6An1HwrbJ89t+8lbdakcw/zbmvTg0oVth9PA0srVauAy+vaK3QB7gK1rfLq7RcNyrgN68Z1ul3z0e6xwLZpeXQLdpalQdGWeCiu1+n/b5C2voHIwRjou4ltsL//AOv26Ma2JjuNCp4LAvu1YGvh9L4ksG6/79aoYa9SGV2KpAbMnCqji7TQ1mwKpu0+bkR1ommE07g+kTexClG7fTRwT812ZrHbYH/3/ucaIUTUFfs1t912v3Kdszmvswz7YOXGfUCEF1Zt3Q44utNtLK/ueRUl20JtJI1x8yMQw5IUmvKoayjXDgUKIZ/3OBKuFwD2tH1Wh5s1VyStDCxPhFG90vD+ovvNQjVptyfUMNWvTjuwJ7EPWqr/3m8bOVSNDwdOdb+Hi6SVgKVs/66GzTcAG3iApHVJE23fWrOtWey2G2VUyJX0joGOiaSP2f6P+q3OQ7vP2aG4ztqFpJtsT0rLE4liFnvYfjCte8b2Eh1sYqGLKCE6hVa4gVQnWdItRILlX4lky2mOko9dQXqQV5VuFiDibz9NXwnH0bbfP8jPB7N5s+1NksN8NhGK0ihNPrqL9sG2wL8S4SciaiZPpi8ZVMACtndu1rCktxPhSQPVujfR8amjYHkQ8GYiBGpsei0ALCvpQdt1QsugTwlyLPBdmH1+nEfEoNalbXY1cJ32c2iTMFl/p0ZtEo9KbAeMl/RX4L+Bux1hG8cBtxAibs2y3CBOeKtO6BMMXsZ1vKQF3UQ1nYzXwoVEDfQ5HDvgOdsvpk5VLQefqNc/SdK1DuG3K21vQ+iDdI2Dn+OczX2dZaDtQmqF4Utx8Aut0DiKOpNIqn0zER94KTHt3S001r7/DKE0e3X6bhp9gkTNMD29HwLcYvs2RenMOwnhrDuJTkU3cAdzVt+5k4gXbhTRqlvZYSpRGnUrora4CEn2qfRNfw8mXT8vjrb9t/RwP9z2l1NS78N1jKl31DBzCpPlEo9anejgzyIqSS1PdCxPlvQ48JDtc+q0mbxO6DclLZfafr/tpyQtA5xK8+dtrmshi2MnaQrwsqQ3AWP7vXdNlb1c5yx5xb5ykENIrTBMKQ5+oRUabyTTiZH7M9Oo9j6SxrcSStFO0sPwPABJB6YRxVnp8yw3p7RaUY38TSEqI0BUafmgpDtsd4tzDyHItZikC4njtj7wdaKKzO+BW23/uabtP9neT9INjtKAJEesWv5NswYlrZ3aaUk7EaOXj0na0/aP1Hxt/YpeUcPMVacd8jk12xLhDSsTI6uLEvkozxOVPmopbOZ0Qm3PkvRcauN7gUMkLZjaflCN+1fbr4VE2x07SVsRib9jiaTiNdP72ul9pcF/PeTkOmdzXmc5aBRSewk4l74k7rpCaoVhSnHwC63QOPqL7emSKkd5Src49wCKesSvEW2upMNJn5evaXacpIOBLxOS3m+hS4VBbM9Q1KR/K1FSbWdiBmMnoqNyahoAnFLH/DzWNVWbOY3WnQL8C9EBeYYQUJsO3CTpftv3DGpg7pwB3Ec4DDvaPlbSNrb3kLQfUVllqy6wm1OEKItTY/s0QqvhX4CPEVU+DqxsSbpM0ga259vJzeWEJqfwHuK4PEnsyzcQs1ivEhVJpg9qYHDaei00kMOxuwX4DfAj27tLuiq9X5Leb67Z1hzkcsRzXmc5yCGkVhimdM0UXGF4YfvRTrehH5UyHwO81+U1YuT+XGLkcs8W7eXmUeAo4kF5QMoP2IWoxHAncEAbtzV73zrqLM83tl+yvS3hzL1AOIvj0wjlx4ga5nWplCA/BdylUII8StIXiLCtVtUw22U3pwhRLpEn0uj3UcD5REWapdL6PYlQmt2aNHkLcALwqu3dgfvS+13p/aE67UwzdpsSeUOPp+2sCuxre9fUzu+onmp0f2pfC402bF9C6DXcRzh2p9n+BtEpqdPOdYHbgQ0knUWIcp0FvDu9r5Xeu4Fc52wnxL5aIYeQWmGYUhz8QtNIGi1pKbp0tHogbJ9p+1zb5wL/sH1ew+d/zuv3gzCTCMF4ATi7X9JnV+0bSe8FjkwfFwdWlnQQMWo5mkg2bFV9uL9KcGvG7JcIYbL32X42OVtfbjFxuVfUMKs67QcAb1JDnXZFrfYziPjzOmRxahTid+sBM4gQsA8BxysS8Pci4vI/16TZbE6o7aeJWaynifCOpYBPSPo0cV3/iEjqrmV+kOW6tN2xs30nMaP3UeJ4/YoIL9uSmCnYlNC46AZyOeI5r7Mc3CbpxvQMPpA+IbUTiCTrZkUFC8OYEqJTqMMKhDDKMgpxo0YRpq5ybCtS0tyGRPuW7vd13TYrVbDYh3gQvBtYV9L1wDrpfYrt5+u2u43MBP6LiBV/gFCafJJwxp4npqe/SYRDNMvy6X9dXCHytDDxsLycEJP6cbM5DpJuIxzl9YCrUryxCMdmRduP1Ggn9IgapvOKfeUSj9qXGDQycY49TTgcXyTOiXVs39WMQdt3SnorkedyHH1O6EnpT75OdFDrshCwsu2H0nZOI8KsVgb2tv3dJu21/VpI5FDIxbYljSES7mcQYXFPOhR9u4ks52zm66ytKJOQWmH4Uhz8QtPYfhjYpPosaXnigVbdFLuRBYnqPjOBH0n6MBHuYULgpg4vANi+R9Lv0g13TSJ0ZxYwtkuce2zfJem3RAjJ24njdAlwaPq8CKFOXMf22wZan2L+DwLulLRhk47NlsSxuh7Yhr6E5j0IR/JrddpKD6lhOmqc70HUaX+dYyzpU3Uamsupsf39dA/YlIiRP4pINt6NGAmfKmkn2//bpN22O6GK0rZXEB2ztSUdAyxDdNJ/T5SlfKxZuzmuhZyOnULF/APAXrZfkvQ0Ue7368BPq/juTpPTEc91nbWblNO2S8OqH9ienSch6QZg++LcF2bjLlDbKq/h8SIeOOt1uh0d+t/fQpTLHNew7shOt2uw40PE4ANsnN4/QtTBz7HdVVr47Vap3aOIcJ2VSOrDNe2NeDXMhja3XcE07Ye90vJajfcDwnluWuGaCCG5uDo29CXY3gP8W4v//8JE/P1GwGZEZ+Q0YqbgoDbs4637fV6lDTbbppALrNN4LTSsX7I6f7vpleOcLa/yGq6vomRbqI0yKYLmRplUZyWdQiQEbmb7Ikl/t91NpeayI2kS8Dfbf23BxihixPIm4HHbjyhidKa5STGyuWxjxKphViiD4mz6/Ydt/6wdbUz21gEecJS2bVy/JLCJ7ctbsD0e+LAjF6dx/YKEMNwp/bc7D3tbEuE9lxF5GFfafn+agfip7Q/Vbesg21sCWN72H1u0syXw68brIcV4P2n70pYa2UZynbOFwnCkhOgUWiGXImjbUQbVWUkPA88SYQP/JKpbzAAOBC4iYty7BuVT2WzkJCJW+q91DThqk29PVP35qaTziGS/+Xa0BqO/I66RqYZZkUNxFmLE/WeSvkn8/5UzZuA221c0Y8z2H+D1TqjtpyUtK2nHuk6o7VckvU4h1/bLkn7YjHOfeJX4n39OaE+8VaE9IWAzSafaPqzZdqZOTpXfIeBZh27FFsBXJP2uxc7DWOAsSbs0/M+3ENdJ1zj45DtnC4VhR3HwC7VQJkXQjORQnX2ASPq6jqjbDnM6NN0kkALzVtlci3oJpgAotAUes31V601llu0LJV1GJGm+kxYTuAdxxEecGqbyKs5C37W2MRHLPoPInfg34IfEfq5D253QNFJ/EbCppCUdlXUqphFlKevwmu19JV3tKLuJpGuJhNtFbL/QpL0zgD/SV3JzKSKE5ueSLkrfNUWaVTiT6PCLiGG/VtLfqj8h8kc6zhCcs4XCsKM4+IW65FIEzcVQqc4eBayRkvbqVMvISS6VTSRtAnyCeiJRA7GqpKqs56vJ7ippnYhY7mOatFnUMIMsirMD8CxRuelVYsT55pQgOt/kdEJTgunG9HVIfpGugS8SMeitlGNFUW5zpRQS+EGig7prjVkBCB2A/Rts31At27ZCjbcpbL8m6StEBwxioOPX9N0r1yaS8ruBoTpnC4VhQ3HwC3XJpQiai6FSnb2F6OxsCdyawX4rtF1lMzlgJxAJpgcDO6pPzRjC+V3A9g+aNP0S8KeGNoqYEflTWh43yO/mRlHDJI/ibIWkleex7aZs5nJCU07HeGB/otMHcc59n5jVmUEIczVL4/U0CniEKGt5FHB+Tee+v935+Tx/RiPMBwBJ5zbmTkj6DOE831bHdjvJec4WCsOV4uAX6lKVw9sYOF2h3HmopHWBZamvCJqLSnV2ZSKsplUV20YaH66PEKON7wd2bOM2ctGqyubCRJnRF4j/ezHglX7265RO/aftiyWtScwQfZYI/7m4hq2KXI54JcLzKNF5gCTCA0yT9ENJ29putq58NtSnOHsacDwR8vF3zak425SzJGlpItxlLUm70aYOdA4nNI16/4E5xR5t+wFJtwOH0TfTN1+khN1dgLuS3alEzHhV3rKd95y2IOk6Ii8H4DVJV9B3T1gA+KqkzVvomLSNHOdsoTCcKQ5+oS5HABvQp9xpXq/ceWOH2jYQM4HtiVj7s23fIamqIVzXEVmJeKjsRtRhBngH4Ry81EJbc9M2lU3bzwK7S9qJSLTevl8cc11GSdqOcO6/aPt/JLXqMOZyxHMJR2VBAyvOniRpDeK83YGIP28K208C71UIPW1K5E0cQ5xjqyfnkRpJzG13QpMzvitRT34dSZ8EFkmJwS8QSd27EzXX55exxD7dDbiA6OCvApyhSO5vqdJNDhoq/Bxh+3iFuvU5NTv72ch1zhYKw5ni4BfqkksRNBdtV511P1EbSacSZfF+LGkbwml8XxflIuRS2cT2JSk052eStupfxq4ZFGUy/5PoIL4nHTfRmmIpFDXMin1ps+JsP16xfYika4jwrV2IWGmo8czJ4YQ6RIMOgdkdiCfoU7XdRZGQfQFNOPjp3nG4pAnECPOHiZK5+8/9l/PF2NTWBYjO6eKSNkrfLQUsV8eopEuJGbf3SXobURd/V0mVyNc/bX+mpZa3h33Je84WCsOOUge/UAtJixIj1ZsA3yLiWQ8DbicelMe7oTZ+p5F0qe0d0/I5hJM3lTlVZ59ocRtrA3+2/Vr6/CY3qdrZCdSnsrkFkXRaOzlY0plE6brvtql5lV0Bk21f26KdxZjTEX+UOR3x39iuVTlFUSJ2A9uvK7soaaLtrsnJSA7zjwkF3p2Ja+GbxIj4NcDOdc9dSbfanpiWpxCx0tu10NbZTihRQWUd4HH6lGZrOaGSvkE4jFsB6xKzAkcTys4fJirz7Gr71Rq21yCqvqzYjnuAohDAhHT+nkxfYjDE/3Cz7Ytq2F2RmN08n5h5WJAo8bkd0aG+nMhN+X2L/0LLzOOcnQo0rZJcKAxnioNfqIWkscBqxIN2lu1nJE2xfbmkrYlSlM2WghsSUoLtZkSozqtp3ZG2v9bZlnUWSau4BYGqZOONRFLtQ/P84w7RS454LtL1u5vt8xUVkJ6zfW+aPTkSOLmOYzvIttZvZXQ1pxOakjY/S6jWvpjW/SuRl3F4K53dXiEd862r60HSzsAv2nX820W/c3YtYEHb96Xv3g38vtvaXCh0kuLgF2qhHlTubEQjXHVWbVCcHcDmGGBz21P7r69mNbqZNJOxqEeAGqakd1T/p5IIXFpeiag0tYztHVrcxjpOIlWtMogTejuwuFtUcR1gW0sCb7d9SzvttspQXkdqk0Juu5E0zfZktUlErVAYzpQY/ELTqAeVO9VjqrNDQMuKswMwi4g9ngqzO4GnE6Os+7RxOy0h6WIiafPHtq9p+OobhDDTsHfwgW8TibDQl8BKCqs7sCEBvSnSyOokIiHyRYXi6Mr0OWK/t91M4mqjius/0kjts6nC0odoQcVVg6vDTm7FbkZuIOk0SLoF+Atx/f4NmOYmlbiTndwKue0ml4haoTDsKA5+oQ49pdyZ6DXV2WyovYqzs7E9KyXaVlPmPwEuI0pQdhNLEbNPq1QrJB0LPNJYgnGYM6NhWZLe1PB5WfrqzDdLdU29CbiXSLC9Glgc2IbIz2nKwSeDimtmu7loLD87E/gq8GZCgfpSorpMs/TaPqhoWUStUBjuFAe/UIdeU+4cjG5Wnc2C2q842x9LWp4YXTvA9o2ZttMKBh4E1leomW4B3Gr72I62amhpjM0cS8ShQzh6s4gwnTocRugirEk4YRCzZcsCG9k+tIbNtqu4Zrabi8ZjNp0YuT/T9iaS9pE0PlUHaoae2Qdqs4haoTDcKQ5+oQ49pdw5F7pZdbatKJ/ibGX/Q0QZSmw/KumtrZQxzEEqK/huouLTQsDywBuIEJKOC/kMMeMk3Up0bNchykReC1zQYnL8vYTzuTwRsrdii+2ETCquGe3mYg6hLNvTG67hKTWce+iRfaBMImqFwnBm1Lz/pFB4HZVg0N2EUw9JMCiVx1tYUrcp2Vb0V519glCd/WZnmjNkDKQ4u2DDazxzahrMNynh8aPMGf/65lYam4m/EP/ngkT+wRm2D7c9gRA5Oq2jrRtaXrU90fYkQv3zUKLc4G2SWlFgvpmYHXkyvQTsR5SdXF3SiK5UlQvbj3a6DTmx/aTt9xIDR5vSJ6J2PLCepCuUhNQKhUJQRvALdegp5c5Er6rOtgXnU5yFiLO/JE3pI2kZ4AZJPwWOtD1j7j8fGmz/k6ghfrKkLYB/k/Ql29NtnyjpHEm72r6ww00dCsY3LNv2P4DvSroM+E9Jtn1ZDbuvAH8HvkdoZPxr2lbVsR4/yO8Kg5DymhanjFpDm0XUCoXhTLkgCk3j3lPu7EXV2Sy4jYqzDTZn9vv8hKS3Eh29X0napZvq4kvaH9jU9t6SzpBk4Ij0endnWzdkfKlhebbTbfuRNPt2UypJ2Gy4zmOESNY+kl4gkjQPIRzU02qG/2RRcc1ot92sQNxnl5F0EzHrWNHq9dsr+6BiMQDbl0qaBZzgFkTUCoXhTKmDX6iNelgwSD2qOtsulEFxVtKCRC3qdzWs24sQTnpbOzoTrZJmm94IfNr2S2nd+4mp/p/Y/nYn29cJJC1m+7l+61auWXZxf0Id+HpiAGDbVF1pMvB54HLb32nSZi4V1yx2c5OS2BcnQs5usF27Ylmv7oMKtSiiVigMZ4qDXyiMQJRBcVYhkrS87bsb1gk4wvYJ7dpOK0haZKBR5KTt8MFudmZ6CUmLAqu4QTQshZq8w/a9HWvYMCJdW+8s+7NQKAxESbItFEYYqaLO2/o792l9HXtnpsXViITl2aRR+4MUMvMdp9G5V3BAWv9Sce5bR9JyALaft/07Saum0WGIUKASTlGTtC8/mjrSOLhX0vhUWaau3RKqWygMQ4qDXyiMPCrFWSBGtSWdReRR1OG9DXZ3kXSWpLMlnZfyG56iO3UGBIykyjlDwewEZUkT0+fK6X+RSL4v1GNFQsF1SrUihUleRmv5dLNr30u6JSWbHytpv3nVni8UCt1L6bkXCiOMDIqz96fKORDOwtmE8zyKKKc603bX1ZlP+6ErKvwMI6bD7LrlXyLCnh5v+P5xpRI9HWldjyLpncTzeirwoKSTCP2GNwCH2b6/BfM5FHILhUKHKQ5+oTAyaafi7F+A9xDiUS8Bf2ksjSmpm2cKi6PZXhaQdDowDfgOMCFCxRlD6CS8UJz7WkwiSvz+DTiRcPRXBLYGvijpkBYqgOVQyC0UCh2mOPiFwghCeRRnbwV2BF4jRKSukrQEcBbRgXi5RfuF3mE6cCNRIvUl4FxiNmcMMaNTHMV6nAHcBywK7Gj7WEnb2N5D0n6ELslWNW3nUMgtFAodpjj4hcIIQX2Ks4cAu6bVbyaUR2tj+xpJt9uuBGeqKipfTtv6ZSv2Cz2FktbC1cC/A+NsnwxRjpNUd73QNGMIBdeNgdMl7QocKmldYFkgi3L4cFfILRSGM908dV4oFNrLZcBWVfWcBsXZk1upcpPi+G+RtKykoyWdTSSvLgl8hobEy8Kw5wYA2y/b3h94h6TN03cHAzd1rGW9zRGEc78CMUvyIrAE0WF6In3XFJJGS1qKEqZWKAxLygh+oTBCyKg4uzExlf94cub2bfjuH8CJyXZxJIY/t0m6kSiXuh+hbDtZ0hZEZ2/dDratl7mJUJYd17DuBeBuwuE/ib5qVvNLToXcQqHQYYqDXyiMMJLi7HiIeuXAx5Pi7JWSmlactX16w8fTbP+t4fORkrbuRuc+Jf+Om+cfFuYLSeOBTwIH254p6SNEJZ0qBn8i8GVJezcmYRfmizuACURlm3HE9TsT2JxIbt+pWYO2HwY2qT5XCrmpLn4JpSoUepyiZFsojDB6QXE2FykUaaLtG5KDf6Tt4+f1u0LzSNrd9k8bPo8Ctrd9Weda1Zuk83Y14HFglu1nJE2xfbmkrYFbBlJorrmtopBbKAwDSgx+oTBC6CXF2YyMAQ6XtAGwD/BHAElPSLophSwV2sMFklZr+LwhcHmnGtPjjAMOAFYFrkvn6X2S1iDyHn5S13AuhdxCodBZSohOoTBy6K84uzYRPjEa+BndqzjbTmYRYQmHAn8mOjoXAb8FjiLKfT7QqcYNB6qRZeL58n1gizR6fxYRUvJIJ9vXa0haCDgOWNr23ZL+AZwDPEeUpr0CuL6FTVQKuWOB76ZtvgE4D/hRC3YLhUIHKQ5+oTBy6EnF2TZzJlFP/CfAM0RcM0RS4QO0WDK0AMCngMttz5D0Wlr3GeAq28W5b55FiVKzu0r6KnAPsKHtLSWNBn4FvK+O4cwKuYVCoYOUEJ1CYeRQKc6uQJ/i7AO2/2D7CUbG/eCThBP/WWB7YCVJBxHiX7sDe3SwbcOFRuE0JyGmicDnO9SeXmcUsCUwmVACPoYIlV+VCNn5qO26YnKTgBOA7YjjNhW4mqh+9MVURrNQKPQgZQS/UBg5FMVZeBZ4I/At4O3ADOBJ4K1pfQnPaYGU8Lm0pC2J2aFJRMWXk4HNJI2xfVUn29iDvEqE4dxNhNFVrA4cm9b/tqbtnAq5hUKhgxQHv1AYIRTFWSDyDdYjFH1PACbZvljSrcCqaXS0UJ+NCAGmDQkHf0GiU7VB+rwwUBz85niaCKF7lBjFh8iFnQZMk/RDSdva/kUN2x1RyC0UCvkpDn6hMEJIirNnJtGhQ4hqOiYcr73pi0cfzlxFhOh8j3D2F5L0HmKU9EpJO5QyjvWxfbSk9W0fB7NH9EcD/2P7/M62rmdZiRhJv5YIefoSEaJzSfr+JOBwoI6DfwTR+aoUcs3rFXJvbKHthUKhQxQHv1AYORTF2Yix/zqwJ+HcXEjE3l8DXEpUDbmsU40bJqhh+SkiLOwiSS/avmSQ3xQGwfbDkjYFLiGSw+8nksUrQaqHgDVqms+hkFsoFLqA4uAXCiOEXlWcbScpmXh/SZNt/xVo3CdIOqIjDRte/AZmCyaNSqq2ewG3SvqN7f/rbPN6D9vPSdoD2MD2Xf2/l/SpmqbbrpBbKBS6g6JkWygUCoW2k/I7NiGqskCMBN9te/rgvyoMJUOpkFsoFIaW4uAXCoVCoS2k6jnYnirpaeB39IXsLAE8YHvXDjWv0A9JiwDHEEJ3PyBC114mZvcfBi60vX3nWlgoFOpSQnQKhUKh0C5+DZwtaXHgLttbVF9IWhK4Q9KoESCo1vUMgUJuoVDoIGUEv1AoFAptI4V97E6MBp8CHE88azboaMMKcyBpOeCdwK4kJWsGUMhtQUSrUCh0kJGgXFkoFAqFIUDSh4G9iOouIsJy9icqsxS6i5wKuYVCocOUEJ1CoVAotIuZzFkmE2AWUV+90F3kVMgtFAodpozgFwqFQqEt2L4IOA+4Na2aAfwYWLNjjSoMRqWQezfh1ENSyLU9EVhYUlGyLRR6lBKDXygUCoW2IGkM8HPgl8DOtrfscJMKgyDpjcQI/rXEDMvLwETg+fQnJwGHl6pHhUJvUhz8QqFQKLQFSW8BNrF9hqSngHsbvyZGiDfvSOMKr0PSYsypkPsocyrk/sb2uzrXwkKhUJfi4BcKhUKh7SShqxnMGX8/xvaLHWpSYQAkvYFQyL1ygO8m2r51gJ8VCoUupzj4hUKhUCgUCoXCMKIk2RYKhUKhUCgUCsOI4uAXCoVCoVAoFArDiOLgFwqFQqFQKBQKw4ji4BcKhUKhUCgUCsOI4uAXCoVCoVAoFArDiP8HzDfHjpzwlioAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "plt.figure(figsize=(13,10))\n",
    "\n",
    "corrmat = user_info.corr()\n",
    "mask = np.array(corrmat)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sns.heatmap(corrmat,mask=mask,vmax=.8,square= True,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "982ff460",
   "metadata": {},
   "outputs": [],
   "source": [
    "def higt_cor(x,y=0.7):\n",
    "    data_cor = (x.corr()>y)\n",
    "    a=[]\n",
    "    \n",
    "    for i in data_cor.columns:\n",
    "        if data_cor[i].sum()>=2:\n",
    "            a.append(i)\n",
    "\n",
    "    return a  #这些是我们要考虑删除的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "d078122e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['贷款总次数', '无效贷款总次数', '已批准贷款总额', '已发放贷款总额', '用户资产', '还款总期数']"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "higt_cor(user_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "f51fc3f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比例</th>\n",
       "      <th>地区</th>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <th>是否填写护照</th>\n",
       "      <th>信用评分</th>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <th>平均贷款期限</th>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <th>...</th>\n",
       "      <th>贷款总次数</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已还贷款比例</th>\n",
       "      <th>贷款与已批准贷款比例</th>\n",
       "      <th>工作类型</th>\n",
       "      <th>有效贷款总次数</th>\n",
       "      <th>贷款通过率</th>\n",
       "      <th>用户资产</th>\n",
       "      <th>年龄区间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65325</td>\n",
       "      <td>89.55</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2054139</td>\n",
       "      <td>34455</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>2.343846e+06</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13814.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.905287e+04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>379</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>467161</td>\n",
       "      <td>12863</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>7.188286e+05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>79605</td>\n",
       "      <td>82.91</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>749</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>16225</td>\n",
       "      <td>1475</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.205642e+04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62371</td>\n",
       "      <td>89.79</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>12991</td>\n",
       "      <td>3207</td>\n",
       "      <td>7.70</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.154741e+05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199709</th>\n",
       "      <td>63387</td>\n",
       "      <td>88.35</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>72033</td>\n",
       "      <td>5354</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.745027e+04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199711</th>\n",
       "      <td>73701</td>\n",
       "      <td>88.19</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>726</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>128709</td>\n",
       "      <td>354750</td>\n",
       "      <td>1.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>2.487053e+05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199712</th>\n",
       "      <td>60424</td>\n",
       "      <td>62.89</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>85</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>592668</td>\n",
       "      <td>0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.705673e+05</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199714</th>\n",
       "      <td>62710</td>\n",
       "      <td>89.30</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>771</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1185601</td>\n",
       "      <td>2500</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.406028e+06</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199716</th>\n",
       "      <td>72000</td>\n",
       "      <td>89.58</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>708</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>97963</td>\n",
       "      <td>0</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.214401e+05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100025 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         资产成本  贷款与资产比例  地区  是否出具驾驶证  是否填写护照  信用评分  近六个月新贷款次数  近六个月违约次数  \\\n",
       "1       65325    89.55   6        0       0   300          0         0   \n",
       "3       99750    73.68   8        0       0   763          0         0   \n",
       "4       65450    79.45  17        0       0   379          0         1   \n",
       "5       79605    82.91   3        0       0   749          1         0   \n",
       "6       62371    89.79   6        0       0   300          0         0   \n",
       "...       ...      ...  ..      ...     ...   ...        ...       ...   \n",
       "199709  63387    88.35  15        0       0   300          1         1   \n",
       "199711  73701    88.19   8        0       0   726          5         0   \n",
       "199712  60424    62.89   3        0       0   753          0         0   \n",
       "199714  62710    89.30   6        0       0   771          1         0   \n",
       "199716  72000    89.58   4        0       0   708          1         0   \n",
       "\n",
       "        平均贷款期限  第一次贷款距今时间  ...  贷款总次数  尚未还清有效贷款总额  每月还款总额  贷款与已还贷款比例  \\\n",
       "1           27         64  ...      7     2054139   34455       0.99   \n",
       "3           25         25  ...      1           0       0   13814.00   \n",
       "4            4         16  ...     16      467161   12863       1.18   \n",
       "5           13         24  ...      2       16225    1475       1.09   \n",
       "6           17         30  ...      3       12991    3207       7.70   \n",
       "...        ...        ...  ...    ...         ...     ...        ...   \n",
       "199709      49         18  ...      6       72033    5354       1.11   \n",
       "199711      12         26  ...     22      128709  354750       1.66   \n",
       "199712      53         85  ...      2      592668       0       0.89   \n",
       "199714       7          1  ...      2     1185601    2500       1.03   \n",
       "199716       6          1  ...      3       97963       0       1.09   \n",
       "\n",
       "        贷款与已批准贷款比例  工作类型  有效贷款总次数     贷款通过率          用户资产  年龄区间  \n",
       "1             1.00     1        2  0.285714  2.343846e+06     5  \n",
       "3             1.00     0        1  1.000000  1.905287e+04     1  \n",
       "4             1.00     1        1  0.062500  7.188286e+05     4  \n",
       "5             1.00     0        1  0.500000  2.205642e+04     2  \n",
       "6             1.00     1        1  0.333333  1.154741e+05     4  \n",
       "...            ...   ...      ...       ...           ...   ...  \n",
       "199709        1.07     0        3  0.500000  9.745027e+04     3  \n",
       "199711        1.00     1        7  0.318182  2.487053e+05     4  \n",
       "199712        1.00     0        2  1.000000  8.705673e+05     3  \n",
       "199714        1.00     1        2  1.000000  1.406028e+06     4  \n",
       "199716        1.00     2        2  0.666667  1.214401e+05     4  \n",
       "\n",
       "[100025 rows x 21 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = ['无效贷款总次数','已批准贷款总额','已发放贷款总额','还款总期数']\n",
    "\n",
    "user = user_info.drop(columns=col)\n",
    "user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "9aa724f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    83515\n",
       "1    16510\n",
       "Name: 是否违约, dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_act_b.是否违约.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "acda7d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16505873531617096"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "16510/user_act_b.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "1f22afc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 100025 entries, 1 to 199716\n",
      "Data columns (total 21 columns):\n",
      " #   Column      Non-Null Count   Dtype  \n",
      "---  ------      --------------   -----  \n",
      " 0   资产成本        100025 non-null  int64  \n",
      " 1   贷款与资产比例     100025 non-null  float64\n",
      " 2   地区          100025 non-null  int64  \n",
      " 3   是否出具驾驶证     100025 non-null  int64  \n",
      " 4   是否填写护照      100025 non-null  int64  \n",
      " 5   信用评分        100025 non-null  int64  \n",
      " 6   近六个月新贷款次数   100025 non-null  int64  \n",
      " 7   近六个月违约次数    100025 non-null  int64  \n",
      " 8   平均贷款期限      100025 non-null  int64  \n",
      " 9   第一次贷款距今时间   100025 non-null  int64  \n",
      " 10  贷款查询次数      100025 non-null  int64  \n",
      " 11  贷款总次数       100025 non-null  int64  \n",
      " 12  尚未还清有效贷款总额  100025 non-null  int64  \n",
      " 13  每月还款总额      100025 non-null  int64  \n",
      " 14  贷款与已还贷款比例   100025 non-null  float64\n",
      " 15  贷款与已批准贷款比例  100025 non-null  float64\n",
      " 16  工作类型        100025 non-null  int64  \n",
      " 17  有效贷款总次数     100025 non-null  int64  \n",
      " 18  贷款通过率       100025 non-null  float64\n",
      " 19  用户资产        100025 non-null  float64\n",
      " 20  年龄区间        100025 non-null  int64  \n",
      "dtypes: float64(5), int64(16)\n",
      "memory usage: 16.8 MB\n"
     ]
    }
   ],
   "source": [
    "user.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "a813ba1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "资产成本          False\n",
       "贷款与资产比例       False\n",
       "地区            False\n",
       "是否出具驾驶证       False\n",
       "是否填写护照        False\n",
       "信用评分          False\n",
       "近六个月新贷款次数     False\n",
       "近六个月违约次数      False\n",
       "平均贷款期限        False\n",
       "第一次贷款距今时间     False\n",
       "贷款查询次数        False\n",
       "贷款总次数         False\n",
       "尚未还清有效贷款总额    False\n",
       "每月还款总额        False\n",
       "贷款与已还贷款比例      True\n",
       "贷款与已批准贷款比例    False\n",
       "工作类型          False\n",
       "有效贷款总次数       False\n",
       "贷款通过率         False\n",
       "用户资产          False\n",
       "年龄区间          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isinf(user).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "5356316e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比例</th>\n",
       "      <th>地区</th>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <th>是否填写护照</th>\n",
       "      <th>信用评分</th>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <th>平均贷款期限</th>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <th>贷款查询次数</th>\n",
       "      <th>贷款总次数</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已批准贷款比例</th>\n",
       "      <th>工作类型</th>\n",
       "      <th>有效贷款总次数</th>\n",
       "      <th>贷款通过率</th>\n",
       "      <th>用户资产</th>\n",
       "      <th>年龄区间</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65325</td>\n",
       "      <td>89.55</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2054139</td>\n",
       "      <td>34455</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>2.343846e+06</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>99750</td>\n",
       "      <td>73.68</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>763</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.905287e+04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65450</td>\n",
       "      <td>79.45</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>379</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>467161</td>\n",
       "      <td>12863</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>7.188286e+05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>79605</td>\n",
       "      <td>82.91</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>749</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16225</td>\n",
       "      <td>1475</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.205642e+04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62371</td>\n",
       "      <td>89.79</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12991</td>\n",
       "      <td>3207</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.154741e+05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199709</th>\n",
       "      <td>63387</td>\n",
       "      <td>88.35</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>72033</td>\n",
       "      <td>5354</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.745027e+04</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199711</th>\n",
       "      <td>73701</td>\n",
       "      <td>88.19</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>726</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>128709</td>\n",
       "      <td>354750</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>2.487053e+05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199712</th>\n",
       "      <td>60424</td>\n",
       "      <td>62.89</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>592668</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.705673e+05</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199714</th>\n",
       "      <td>62710</td>\n",
       "      <td>89.30</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>771</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1185601</td>\n",
       "      <td>2500</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.406028e+06</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199716</th>\n",
       "      <td>72000</td>\n",
       "      <td>89.58</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>708</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>97963</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.214401e+05</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100025 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         资产成本  贷款与资产比例  地区  是否出具驾驶证  是否填写护照  信用评分  近六个月新贷款次数  近六个月违约次数  \\\n",
       "1       65325    89.55   6        0       0   300          0         0   \n",
       "3       99750    73.68   8        0       0   763          0         0   \n",
       "4       65450    79.45  17        0       0   379          0         1   \n",
       "5       79605    82.91   3        0       0   749          1         0   \n",
       "6       62371    89.79   6        0       0   300          0         0   \n",
       "...       ...      ...  ..      ...     ...   ...        ...       ...   \n",
       "199709  63387    88.35  15        0       0   300          1         1   \n",
       "199711  73701    88.19   8        0       0   726          5         0   \n",
       "199712  60424    62.89   3        0       0   753          0         0   \n",
       "199714  62710    89.30   6        0       0   771          1         0   \n",
       "199716  72000    89.58   4        0       0   708          1         0   \n",
       "\n",
       "        平均贷款期限  第一次贷款距今时间  贷款查询次数  贷款总次数  尚未还清有效贷款总额  每月还款总额  贷款与已批准贷款比例  \\\n",
       "1           27         64       0      7     2054139   34455        1.00   \n",
       "3           25         25       0      1           0       0        1.00   \n",
       "4            4         16       0     16      467161   12863        1.00   \n",
       "5           13         24       0      2       16225    1475        1.00   \n",
       "6           17         30       0      3       12991    3207        1.00   \n",
       "...        ...        ...     ...    ...         ...     ...         ...   \n",
       "199709      49         18       1      6       72033    5354        1.07   \n",
       "199711      12         26       0     22      128709  354750        1.00   \n",
       "199712      53         85       0      2      592668       0        1.00   \n",
       "199714       7          1       0      2     1185601    2500        1.00   \n",
       "199716       6          1       0      3       97963       0        1.00   \n",
       "\n",
       "        工作类型  有效贷款总次数     贷款通过率          用户资产  年龄区间  \n",
       "1          1        2  0.285714  2.343846e+06     5  \n",
       "3          0        1  1.000000  1.905287e+04     1  \n",
       "4          1        1  0.062500  7.188286e+05     4  \n",
       "5          0        1  0.500000  2.205642e+04     2  \n",
       "6          1        1  0.333333  1.154741e+05     4  \n",
       "...      ...      ...       ...           ...   ...  \n",
       "199709     0        3  0.500000  9.745027e+04     3  \n",
       "199711     1        7  0.318182  2.487053e+05     4  \n",
       "199712     0        2  1.000000  8.705673e+05     3  \n",
       "199714     1        2  1.000000  1.406028e+06     4  \n",
       "199716     2        2  0.666667  1.214401e+05     4  \n",
       "\n",
       "[100025 rows x 20 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "9f88e93b",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_ = user.drop('贷款与已还贷款比例',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "1716e9a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>资产成本</th>\n",
       "      <th>贷款与资产比例</th>\n",
       "      <th>地区</th>\n",
       "      <th>是否出具驾驶证</th>\n",
       "      <th>是否填写护照</th>\n",
       "      <th>信用评分</th>\n",
       "      <th>近六个月新贷款次数</th>\n",
       "      <th>近六个月违约次数</th>\n",
       "      <th>平均贷款期限</th>\n",
       "      <th>第一次贷款距今时间</th>\n",
       "      <th>...</th>\n",
       "      <th>贷款总次数</th>\n",
       "      <th>尚未还清有效贷款总额</th>\n",
       "      <th>每月还款总额</th>\n",
       "      <th>贷款与已批准贷款比例</th>\n",
       "      <th>工作类型</th>\n",
       "      <th>有效贷款总次数</th>\n",
       "      <th>贷款通过率</th>\n",
       "      <th>用户资产</th>\n",
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       "      <th>是否违约</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>62575</td>\n",
       "      <td>73.51</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>25</td>\n",
       "      <td>...</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68930</td>\n",
       "      <td>81.24</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
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       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74500</td>\n",
       "      <td>76.51</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72000</td>\n",
       "      <td>84.72</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69640</td>\n",
       "      <td>81.85</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.000000e+00</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33015</th>\n",
       "      <td>85955</td>\n",
       "      <td>70.73</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>333</td>\n",
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       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>65628</td>\n",
       "      <td>24638</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.111111</td>\n",
       "      <td>1.453244e+05</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33016</th>\n",
       "      <td>64217</td>\n",
       "      <td>69.92</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>46388</td>\n",
       "      <td>23900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6.737257e+04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33017</th>\n",
       "      <td>77030</td>\n",
       "      <td>89.96</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>737</td>\n",
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       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>35911</td>\n",
       "      <td>2239</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.202439e+04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33018</th>\n",
       "      <td>69990</td>\n",
       "      <td>78.58</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>547</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>4117396</td>\n",
       "      <td>349618</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>1.108090e+07</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33019</th>\n",
       "      <td>64063</td>\n",
       "      <td>84.29</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>417</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>4279</td>\n",
       "      <td>9923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>4.101751e+04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33020 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        资产成本  贷款与资产比例  地区  是否出具驾驶证  是否填写护照  信用评分  近六个月新贷款次数  近六个月违约次数  平均贷款期限  \\\n",
       "0      62575    73.51   1        0       0    18          0         0      25   \n",
       "1      68930    81.24   4        0       0    18          0         0      12   \n",
       "2      74500    76.51  14        0       0    17          0         0      13   \n",
       "3      72000    84.72   4        0       0   825          0         0       1   \n",
       "4      69640    81.85   6        0       0   825          0         0      17   \n",
       "...      ...      ...  ..      ...     ...   ...        ...       ...     ...   \n",
       "33015  85955    70.73   6        0       0   333          0         2      12   \n",
       "33016  64217    69.92   6        0       0   359          1         1      16   \n",
       "33017  77030    89.96   4        0       0   737          0         0      17   \n",
       "33018  69990    78.58   3        0       0   547          2         4      15   \n",
       "33019  64063    84.29   6        0       0   417          0         1      18   \n",
       "\n",
       "       第一次贷款距今时间  ...  贷款总次数  尚未还清有效贷款总额  每月还款总额  贷款与已批准贷款比例  工作类型  有效贷款总次数  \\\n",
       "0             25  ...      1           0       0         1.0     0        0   \n",
       "1             12  ...      1           0       0         1.0     0        0   \n",
       "2             13  ...      1           0       0         1.0     0        0   \n",
       "3             14  ...      2           0       0         1.0     1        0   \n",
       "4             17  ...      1           0       0         1.0     1        0   \n",
       "...          ...  ...    ...         ...     ...         ...   ...      ...   \n",
       "33015         24  ...      9       65628   24638         1.0     0        1   \n",
       "33016         31  ...      4       46388   23900         1.0     1        2   \n",
       "33017         17  ...      1       35911    2239         1.0     1        1   \n",
       "33018         43  ...     18     4117396  349618         1.0     0        8   \n",
       "33019         37  ...     17        4279    9923         1.0     0        2   \n",
       "\n",
       "          贷款通过率          用户资产  年龄区间  是否违约  \n",
       "0      0.000000  0.000000e+00     4     0  \n",
       "1      0.000000  0.000000e+00     5     0  \n",
       "2      0.000000  0.000000e+00     4     0  \n",
       "3      0.000000  0.000000e+00     4     0  \n",
       "4      0.000000  0.000000e+00     4     0  \n",
       "...         ...           ...   ...   ...  \n",
       "33015  0.111111  1.453244e+05     3     1  \n",
       "33016  0.500000  6.737257e+04     4     1  \n",
       "33017  1.000000  6.202439e+04     4     1  \n",
       "33018  0.444444  1.108090e+07     3     1  \n",
       "33019  0.117647  4.101751e+04     4     1  \n",
       "\n",
       "[33020 rows x 21 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.under_sampling  import NearMiss\n",
    "ee =NearMiss(version=1)\n",
    "x_resampled, y_resampled = ee.fit_resample(user_, user_act_b['是否违约'])\n",
    "users =pd.concat([x_resampled,y_resampled],axis=1)\n",
    "users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "fc5fedfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#划分数据集\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "y = users['是否违约']\n",
    "x = users.iloc[:,:-1]\n",
    "\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.3,random_state=420)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "23b982af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "# 模型处理模块\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# 标准化处理模块\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 常规模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# 集成学习和stacking模型\n",
    "from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "# 评价标准模块\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score,recall_score,precision_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "b3157022",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(xtrain, ytrain, xtest, ytest,\n",
    "               model,model_name):\n",
    "    \n",
    "    print('训练{}'.format(model_name))\n",
    "    \n",
    "    clf=model\n",
    "    start = time.time()\n",
    "    clf.fit(xtrain, ytrain.values.ravel())\n",
    "    \n",
    "     #验证模型\n",
    "    print('训练准确率：{:.4f}'.format(clf.score(xtrain, ytrain)))\n",
    "    \n",
    "    \n",
    "    predict=clf.predict(xtest)\n",
    "    score = clf.score(xtest, ytest)\n",
    "    precision=precision_score(ytest,predict)\n",
    "    recall=recall_score(ytest,predict)\n",
    "    print('测试准确率：{:.4f}'.format(score))\n",
    "    print('测试精确率：{:.4f}'.format(precision))\n",
    "    print('测试召回率：{:.4f}'.format(recall))\n",
    "    \n",
    "    end = time.time()\n",
    "    duration = end - start\n",
    "    print('模型训练耗时：{:6f}s'.format(duration))\n",
    "    \n",
    "    \n",
    "    return clf, score,precision,recall, duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "dd0fed5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练LR\n",
      "训练准确率：0.8534\n",
      "测试准确率：0.8568\n",
      "测试精确率：0.9384\n",
      "测试召回率：0.7598\n",
      "模型训练耗时：0.232918s\n",
      "训练DT\n",
      "训练准确率：0.8847\n",
      "测试准确率：0.8686\n",
      "测试精确率：0.9600\n",
      "测试召回率：0.7657\n",
      "模型训练耗时：0.149901s\n",
      "训练AdaBoost\n",
      "训练准确率：0.8638\n",
      "测试准确率：0.8664\n",
      "测试精确率：0.9309\n",
      "测试召回率：0.7880\n",
      "模型训练耗时：1.615928s\n",
      "训练GBDT\n",
      "训练准确率：0.8747\n",
      "测试准确率：0.8737\n",
      "测试精确率：0.9478\n",
      "测试召回率：0.7876\n",
      "模型训练耗时：4.781585s\n",
      "训练RF\n",
      "训练准确率：1.0000\n",
      "测试准确率：0.8712\n",
      "测试精确率：0.9418\n",
      "测试召回率：0.7878\n",
      "模型训练耗时：4.050606s\n",
      "训练XGBoost\n",
      "[19:15:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "训练准确率：0.9107\n",
      "测试准确率：0.8697\n",
      "测试精确率：0.9418\n",
      "测试召回率：0.7845\n",
      "模型训练耗时：1.952983s\n"
     ]
    }
   ],
   "source": [
    "model_name_param_dict = {    'LR': (LogisticRegression(penalty =\"l2\")),\n",
    "                             'DT': (DecisionTreeClassifier(max_depth=10,min_samples_split=10)),\n",
    "                             'AdaBoost': (AdaBoostClassifier()),\n",
    "                             'GBDT': (GradientBoostingClassifier()),\n",
    "                             'RF': (RandomForestClassifier()),\n",
    "                             'XGBoost':(XGBClassifier())\n",
    "                         }\n",
    "\n",
    "result_df = pd.DataFrame(columns=['Accuracy (%)','precision(%)','recall(%)','Time (s)'],\n",
    "                             index=list(model_name_param_dict.keys()))\n",
    "\n",
    "for model_name, model in model_name_param_dict.items():\n",
    "    clf, acc,pre,recall, mean_duration = train_model(xtrain, ytrain,\n",
    "                                                        xtest, ytest,\n",
    "                                                        model,model_name)\n",
    "    result_df.loc[model_name, 'Accuracy (%)'] = acc\n",
    "    result_df.loc[model_name, 'precision(%)'] = pre\n",
    "    result_df.loc[model_name, 'recall(%)'] = recall\n",
    "    result_df.loc[model_name, 'Time (s)'] = mean_duration \n",
    "\n",
    "result_df.to_csv(os.path.join('model_comparison.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "dca741f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 10, 'max_features': 10, 'n_estimators': 300}"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'n_estimators': [50,100,300], 'max_features': [10,20,30,40,50,60],\"max_depth\":[4,6,8,10,12]}\n",
    "model = RandomForestClassifier()\n",
    "gridsearch = GridSearchCV(model,param_grid,cv=5,scoring='roc_auc')\n",
    "result = gridsearch.fit(xtrain,ytrain)\n",
    "result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "3fb81ab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9198112936104158"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "582c882f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9582809751193767, 0.7794358135731807)"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre = result.predict(xtest)\n",
    "precision=precision_score(ytest,pre)\n",
    "recall=recall_score(ytest,pre)\n",
    "precision,recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "8f61844a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:02:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:02:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:02:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:03:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:03:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:04:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:04:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:05:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:05:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:06:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:07:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:07:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:08:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:08:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:09:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:10:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:10:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:11:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:11:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:12:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:13:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:13:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:13:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:13:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:13:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:13:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:13:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:13:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:14:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:14:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:14:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:15:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:16:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:34] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:16:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:16:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:17:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:18:24] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:18:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:19] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:26] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:31] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:19:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:19:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:02] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:20:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:21:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:21:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:21:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:21:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:21:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:21:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:21:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:21:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:21:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:11] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:18] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:32] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:22:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:00] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:23:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:09] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:15] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:17] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:22] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:33] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:36] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:23:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:04] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:24:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:10] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:16] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:23] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:30] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:24:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:25:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:25:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:25:20] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:25:27] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:25:35] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:25:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:25:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:25:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:05] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:21] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:37] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:38] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:39] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:26:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:42] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:43] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:44] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:45] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:46] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:47] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:48] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:50] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:51] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:52] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:53] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[20:26:59] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.5.1/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24220/3131123972.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mXGBClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mgridsearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'roc_auc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgridsearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxtrain\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mytrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    889\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 891\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[1;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1390\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1391\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1392\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    836\u001b[0m                     )\n\u001b[0;32m    837\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 838\u001b[1;33m                 out = parallel(\n\u001b[0m\u001b[0;32m    839\u001b[0m                     delayed(_fit_and_score)(\n\u001b[0;32m    840\u001b[0m                         \u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbase_estimator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;31m# change the default number of processes to -1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m             return [func(*args, **kwargs)\n\u001b[0m\u001b[0;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    678\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    679\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 680\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    682\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    504\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    505\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 506\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    508\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\sklearn.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)\u001b[0m\n\u001b[0;32m   1248\u001b[0m         )\n\u001b[0;32m   1249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1250\u001b[1;33m         self._Booster = train(\n\u001b[0m\u001b[0;32m   1251\u001b[0m             \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1252\u001b[0m             \u001b[0mtrain_dmatrix\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\training.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks)\u001b[0m\n\u001b[0;32m    186\u001b[0m     \u001b[0mBooster\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0ma\u001b[0m \u001b[0mtrained\u001b[0m \u001b[0mbooster\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m     \"\"\"\n\u001b[1;32m--> 188\u001b[1;33m     bst = _train_internal(params, dtrain,\n\u001b[0m\u001b[0;32m    189\u001b[0m                           \u001b[0mnum_boost_round\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_boost_round\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m                           \u001b[0mevals\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\training.py\u001b[0m in \u001b[0;36m_train_internal\u001b[1;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks, evals_result, maximize, verbose_eval, early_stopping_rounds)\u001b[0m\n\u001b[0;32m     79\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbefore_iteration\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     80\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m         \u001b[0mbst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     82\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mafter_iteration\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\xgboost\\core.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[0;32m   1678\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1679\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1680\u001b[1;33m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,\n\u001b[0m\u001b[0;32m   1681\u001b[0m                                                     \u001b[0mctypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_int\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1682\u001b[0m                                                     dtrain.handle))\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "param_grid = {'n_estimators': [50,100,300],\"max_depth\":[6,8,10,12],\n",
    "             \"subsample\": [0.5,0.6,0.7,0.8],\"colsample_bytree\": [0.5,0.6,0.7,0.8]}\n",
    "model = XGBClassifier()\n",
    "gridsearch = GridSearchCV(model,param_grid,cv=5,scoring='roc_auc')\n",
    "result = gridsearch.fit(xtrain,ytrain)\n",
    "result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "972cd3fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#太慢了跑不动了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "f0252cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.model']"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#优质模型保存\n",
    "import joblib\n",
    "#保存模型\n",
    "joblib.dump(result,'model.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94d9fd7c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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